Method of and system for analyzing, modeling and valuing elements of a business enterprise

ABSTRACT

An automated system ( 100 ) and method for analyzing, modeling and valuing elements of a business enterprise on a specified valuation date. The performance of the elements are analyzed using search algorithms and induction algorithms to determine the value drivers associated with each element. The induction algorithms are also used to create composite variables that relate element performance to enterprise revenue, expenses and changes in capital. Predictive models are then used to determine the correlation between the value drivers and the enterprise revenue, expenses and changes in capital. The correlation percentages for each value driver are then multiplied by capitalized value of future revenue, expenses and changes in capital, the resulting numbers for each value driver associated with each element are then added together to calculate a value for each element.

CROSS REFERENCE TO RELATED PATENT

This application is a continuation of application Ser. No. 09/938,874filed Aug. 27, 2001. Application Ser. No. 09/938,874 is a continuationof application Ser. No. 08/999,245 filed Dec. 10, 1997 (now abandoned)that was a continuation in part of application Ser. No. 08/779,109 filedJan. 6, 1997 that matured into U.S. Pat. No. 6,393,406 on May 21, 2002.The subject matter of this application is also related to the subjectmatter of U.S. Pat. No. 5,615,109 for “Method of and System forGenerating Feasible, Profit Maximizing Requisition Sets”, by Jeff S.Eder, the disclosure of which is incorporated herein by reference.

BACKGROUND OF THE INVENTION

This invention relates to a method of and system for business valuation,more particularly, to an automated system that analyzes elements of abusiness to identify their value drivers, models the value creationimpact of the elements and computes a valuation of each of the elementson a specified date.

The valuation of a business is a complex and time-consuming undertaking.Business valuations determine the price that a hypothetical buyer wouldpay for a business under a given set of circumstances. The volume ofbusiness valuations being performed each year is increasingsignificantly. A leading cause of this growth in volume is theincreasing use of mergers and acquisitions as vehicles for corporategrowth. Business valuations are frequently used in setting the price fora business that is being bought or sold. Another reason for the growthin the volume of business valuations has been their increasing use inareas other than supporting merger and acquisition transactions. Forexample, business valuations are now being used by financialinstitutions to determine the amount of credit that should be extendedto a company, by courts in determining litigation settlement amounts andby investors in evaluating the performance of company management.

In most cases, a business valuation is completed by an appraiser or aCertified Public Accountant (hereinafter, appraiser) using a combinationof judgment, experience and an understanding of generally acceptedvaluation principles. The two primary types of business valuations thatare widely used and accepted are income valuations and asset valuations.Market valuations are also used in some cases but their use isrestricted because of the difficulty inherent in trying to compare twodifferent companies.

Income valuations are based on the premise that the current value of abusiness is a function of the future value that an investor can expectto receive from purchasing all or part of the business. Incomevaluations are the most widely used type of valuation. They aregenerally used for valuing businesses that are expected to continueoperating for the foreseeable future. In these valuations the expectedreturns from investing in the business and the risks associated withreceiving the expected returns are evaluated by the appraiser. Theappraiser then determines the value whereby a hypothetical buyer wouldreceive a sufficient return on the investment to compensate the buyerfor the risk associated with receiving the expected returns. Incomevaluation methods include the capitalization of earnings method, thediscounted future income method, the discounted cash flow method, theeconomic income method and other formula methods.

Asset valuations consider the business to be a collection of assetswhich have an intrinsic value to a third party in an asset sale. Assetvaluations are typically used for businesses that are ceasing operationand for specific type of businesses such as holding companies andinvestment companies. Asset valuation methods include the book valuemethod, the adjusted book value method, the economic balance sheetmethod and the liquidation method.

Market valuations are used to place a value on one business by usingvaluations that have been established for comparable businesses ineither a public stock market or a recent transaction. This method isdifficult to use properly because no two companies are exactly the sameand no two transactions are completed for the exact same reasons. Marketvaluation methods include the price to earnings method, the comparablesales method, industry valuation methods and the comparable investmentmethod.

When performing a business valuation, the appraiser is generally free toselect the valuation type and method (or some combination of themethods) in determining the business value. Under the currentprocedures, there is no correct answer, there is only the best possibleinformed guess for any given business valuation. There are severaldifficulties inherent in this approach. First, the reliance on informedguessing places a heavy reliance on the knowledge and experience of theappraiser. The recent increase in the need for business valuations hasstrained the capacity of existing appraisal organizations. As a result,the average experience level of those performing the valuations hasdecreased. The situation is even worse for many segments of the Americaneconomy where experienced appraisers don't exist because the industriesare too new. Another drawback of the current procedures for completing avaluation is that the appraiser is typically retained and paid by aparty to a proposed transaction. It is difficult in this situation to becertain that the valuation opinion is unbiased and fair. Given theappraiser's wide latitude for selecting the method, the largevariability of experience levels in the industry and the high likelihoodof appraiser bias, it is not surprising that it is generally verydifficult to compare the valuations of two different appraisers—even forthe same business. These limitations in turn serve to seriously diminishthe usefulness of business valuations to business managers, businessowners and financial institutions.

The usefulness of business valuations to business owners and managers islimited for another reason—valuations typically determine only the valueof the business as a whole. To provide information that would be usefulin improving the business, the valuation would have to furnishsupporting detail that would highlight the value of different elementsof the business. An operating manager would then be able to use a seriesof business valuations to identify elements within a business that havebeen decreasing in value. This information could also be used toidentify corrective action programs and to track the progress that theseprograms have made in increasing business value. This same informationcould also be used to identify elements that are contributing to anincrease in business value. This information could be used to identifyelements where increased levels of investment would have a significantfavorable impact on the overall health of the business.

Another limitation of the current methodology is that financialstatements and accounting records have traditionally provided the basisfor most business valuations Appraisers generally spend a great deal oftime extracting, aggregating, verifying and interpreting the informationfrom accounting systems as part of the valuation process. Accountingrecords do have the advantage of being prepared in a generally unbiasedmanner using the consistent framework of Generally Accepted AccountingPrinciples (hereinafter, GAAP). Unfortunately, these accountingstatements have proved to be increasingly inadequate for use inevaluating the financial performance of modern companies.

Many have noted that traditional accounting systems are drivinginformation-age managers to make the wrong decisions and the wronginvestments. Accounting systems are “wrong” for one simple reason, theytrack tangible assets while ignoring intangible assets. Intangibleassets such as the skills of the workers, intellectual property,business infrastructure, databases, and relationships with customers andsuppliers are not measured with current accounting systems. Thisoversight is critical because in the present economy the success of anenterprise is determined more by its ability to use its intangibleassets than by its ability to amass and control the physical ones thatare tracked by traditional accounting systems.

The recent experience of several of the most important companies in theU.S. economy, IBM, General Motors and DEC, illustrates the problems thatcan arise when intangible asset information is omitted from corporatefinancial statements. All three were showing large profits using currentaccounting systems while their businesses were falling apart. If theyhad been forced to take write-offs when the declines in intangibleassets were occurring, the problems would have been visible to themarket and management would have been forced to act on them much sooner.These deficiencies of traditional accounting systems are particularlynoticeable in high technology companies that are highly valued for theirintangible assets and their options to enter new markets rather thantheir tangible assets.

The accounting profession itself recognizes the limitations oftraditional accounting systems. A group of senior financial executives,educators and consultants that had been asked to map the future offinancial management by the American Institute of Certified PublicAccountants (AICPA) recently concluded that:

-   -   a) Operating managers will continue to lose confidence in        traditional financial reporting systems,    -   b) The motto of CFOs in the future will likely be “close enough        is good enough”, and    -   c) The traditional financial report will never again be used as        the exclusive basis for any business decisions.

The deficiency of traditional accounting systems is also one of the rootcauses of the short term focus of many American firms. Becausetraditional accounting methods ignore intangible assets, expendituresthat develop a market or expand the capabilities of an organization aregenerally shown as expenses that only decrease the current periodprofit. For example, an expenditure for technical training whichincreases the value of an employee to an enterprise is an expense whilean expenditure to refurbish a piece of furniture is capitalized as anasset.

Even when intangible assets have been considered, the limitations in theexisting methodology have severely restricted the utility of thevaluations that have been produced. All known prior efforts to valueintangible assets have been restricted to independent valuations ofdifferent types of intangible assets with only limited attempts tomeasure the actual impact of the asset on the enterprise that owns it.Some of the intangible assets that have been valued separately in thisfashion are: brand names, customers and intellectual property. Problemsassociated with the known methods for valuing intangible assets include:

-   -   1. Interaction between intangible assets is ignored, for example        the value of a brand name is in part a function of the customers        that use the product—the more prestigious the customers, the        stronger the brand name. In a similar fashion the stronger the        brand name, the more likely it will be that customers will stay        a long time. Valuing either of these assets in isolation will        give the wrong answer; and,    -   2. The value of an intangible asset is a function of the benefit        that it provides the enterprise. Therefore, measuring the value        of an intangible asset requires a method for measuring the        actual impact of the asset on the enterprise something that is        missing from known existing methods

The historical dependence on accounting records for valuing businessenterprises has to some extent been a matter of simple convenience.Because corporations are required to maintain financial records for taxpurposes, accounting statements are available for virtually everycompany. At the same time, the high cost of data storage has untilrecently prevented the more detailed information required for valuingintangibles from being readily available. In a similar manner, theabsence of integrated corporate databases within corporations and thehome-grown nature of most corporate systems has until recently made itdifficult to compare similar data from different firms. Unfortunately,even the firms that have established integrated business managementsystems find that retrieving the information required to perform anintegrated analysis of their data is a cumbersome task. These firms alsofind that there are few tools that facilitate the analysis of theinformation after it is gathered together in one place.

The lack of a consistent, well accepted, realistic method for measuringall the elements of business value also prevents some firms fromreceiving the financing they need to grow. Most banks and lendinginstitutions focus on book value when evaluating the credit worthinessof a business seeking funds. As stated previously, the value of manyhigh technology firms lies primarily in intangible assets and growthoptions that aren't visible under traditional definitions of accountingbook value. As a result, these businesses generally aren't eligible toreceive capital from traditional lending sources, even though theirfinancial prospects are generally far superior to those of companieswith much higher tangible book values.

In light of the preceding discussion, it is clear that it would beadvantageous to have an automated financial system that measured thefinancial performance of all the elements of business value for a givenenterprise. Ideally, this system would be capable of generating detailedvaluations for businesses in new industries.

SUMMARY OF THE INVENTION

It is a general object of the present invention to provide a novel anduseful system that calculates and displays a comprehensive and accuratevaluation for the elements of an enterprise that overcomes thelimitations and drawbacks of the prior art that were describedpreviously.

A preferable object to which the present invention is applied is thevaluation of elements of a high technology commercial enterprise where asignificant portion of the business value is associated with intangibleassets.

The present invention eliminates a great deal of time-consuming andexpensive effort by automating the extraction of transaction data fromthe databases, tables, and files of the existing computer-basedcorporate finance, operation, sales, and human resource softwaredatabases as required to operate the system. In accordance with theinvention, the automated extraction, aggregation and analysis oftransaction data from a variety of existing computer-based systemssignificantly increases the scale and scope of the analysis that can becompleted. The system of the present invention further enhances theefficiency and effectiveness of the business valuation by automating theretrieval, storage and analysis of information useful for valuingintangible assets from external databases and publications via theinternet or other external networks.

Uncertainty over which method is being used for completing the valuationand the resulting inability to compare different valuations iseliminated by the present invention by consistently utilizing differentvaluation methodologies for valuing the different elements of theenterprise as shown in Table 1.

TABLE 1 Enterprise element Valuation methodology Excess Cash &Marketable Securities GAAP Total current-operation value (COPTOT):Income valuation* Current-operation: Cash & Marketable Securities GAAP(CASH) Current-operation: Accounts Receivable (AR) GAAPCurrent-operation: Inventory (IN) GAAP Current-operation: PrepaidExpenses (PE) GAAP Current-operation: Production Equipment (PEQ) Ifcorrelation value> liquidation value, then use correlation valuation,else use liquidation value Current-operation: Other Physical Assets(OPA) Liquidation Value Current-operation: Other Assets (OA) GAAPCurrent-operation: Intangible Assets (IA): Customers Correlation tocomponent(s) of value Employees Correlation to component(s) of valueVendor Relationships Correlation to component(s) of value StrategicPartnerships Correlation to component(s) of value Brand NamesCorrelation to component(s) of value Other Intangibles Correlation tocomponent(s) of value Current-operation: General going concern GCV =COPTOT − CASH − AR − IN − value (GCV) PE − PEQ − OPA − OA − IA Growthoptions Option pricing algorithms *The user also has the option ofspecifying the total valueThe value of an enterprise operation is calculated by summing items fromTable 1 as shown in Table 2.

TABLE 2 Enterprise Value = Current value of enterprise excess cash andmarketable securities + Value of current-operation + Value of growthoptionsAs shown in Table 1, the growth opportunities of the firm are valuedusing option pricing algorithms. Option pricing algorithms areimprovements over traditional methods as they correct two inaccurateassumptions implicit in traditional discounted cash flow analyses ofbusiness growth opportunities, namely: the assumption that investmentdecisions are reversible, and the assumption that investment decisionscan not be delayed. In reality, a firm with a project that requires aninvestment has the right but not the obligation to buy an asset at somefuture time of its choosing. However, once the investment is made it isoften irreversible—a situation analogous to a call option. Becauseoption valuation algorithms explicitly recognize that investments ofthis type are often irreversible and that they can be delayed, the assetvalues calculated using these algorithms are more accurate thanvaluations created using more traditional approaches. The use of optionpricing analysis for valuing growth opportunities (hereinafter, growthoptions) gives the present invention a distinct advantage overtraditional approaches to business valuation.

The innovative system has the added benefit of providing a large amountof detailed information concerning both tangible and intangible elementsof enterprise business value. The system also gives the user the abilityto track the changes in elements of business value and total businessvalue over time by comparing the current valuation to previouslycalculated valuations. As such, the system also provides the user withan alternative mechanism for tracking financial performance. Tofacilitate its use as a tool for improving the value of an enterprise,the system of the present invention produces reports in formats that aresimilar to the reports provided by traditional accounting systems. Themethod for tracking the elements of value for a business enterpriseprovided by the present invention eliminates many of the limitationsassociated with current accounting systems that were describedpreviously.

BRIEF DESCRIPTION OF DRAWINGS

These and other objects, features and advantages of the presentinvention will be more readily apparent from the following descriptionof the preferred embodiment of the invention in which:

FIG. 1 is a block diagram showing the major processing steps of thepresent invention;

FIG. 2 is a diagram showing the files or tables in the applicationdatabase of the present invention that are utilized for data storage andretrieval during the processing that values elements of the enterprise;

FIG. 3 is a block diagram of an implementation of the present invention;

FIG. 4 is a diagram showing the data windows that are used for receivinginformation from and transmitting information to the user during systemprocessing;

FIG. 5A, FIG. 5B and FIG. 5C are block diagrams showing the sequence ofsteps in the present invention used for extracting, aggregating andstoring information utilized in system processing from: user input, thebasic financial system database, the operation management systemdatabase, the advanced financial system database, the sales managementsystem database, external databases via the internet and the humanresource system database;

FIG. 6A, FIG. 6B, FIG. 6C, FIG. 6D, FIG. 6E and FIG. 6F are blockdiagrams showing the sequence of steps in the present invention that areutilized in identifying the value drivers and defining the compositevariables;

FIG. 7 is a block diagram showing the sequence of steps in the presentinvention used for the specification and valuation of growth options;

FIG. 8 is a block diagram showing the sequence of steps associated withthe analyzing the components of enterprise value;

FIG. 9A, FIG. 9B and FIG. 9C are block diagrams showing the sequence ofsteps in the present invention that are utilized in the specificationand optimization of the predictive models that determine therelationships between value drivers and the revenue, expense and capitalcomponents of enterprise value;

FIG. 10 is a diagram illustrating the processing of a feed-forwardneural network;

FIG. 11 is a diagram illustrating the processing of a Kohonen neuralnetwork;

FIG. 12 is a block diagram showing the sequence of the steps in thepresent invention used for calculating the percentage of the revenue,expense and capital components attributed to the elements andsub-elements of value;

FIG. 13 is a block diagram showing the sequence of steps in the presentinvention used in preparing, displaying and optionally printing reports;

FIG. 14 is a sample Value Map™ report from the present invention showingthe calculated value for all elements of value in the total company onthe valuation date; and

FIG. 15 is a sample Value Creation report from the present inventiondetailing the changes in the elements of value and total company valuefrom a prior date to the valuation date.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

FIG. 1 provides an overview of the processing completed by theinnovative system for business valuation. In accordance with the presentinvention, an automated method of and system (100) for businessvaluation is provided. Processing starts in this system (100) with ablock of software (200) that extracts, aggregates and stores thetransaction data and user input required for completing a valuation.This information is extracted via an interconnection network (25) from abasic financial system database (10), an operation management systemdatabase (15), an advanced financial system database (30), a salesmanagement system database (35), and a human resource system database(40). Information can also be extracted from an on-line externaldatabase such as those found on an internet (5) via a communicationslink (45). These information extractions and aggregations are guided bya user (20) through interaction with a user-interface portion of theapplication software (900) that mediates the display and transmission ofall information to the user (20) from the system (100) as well as thereceipt of information into the system (100) from the user (20) using avariety of data windows tailored to the specific information beingrequested or displayed in a manner that is well known. While only onedatabase of each type (10, 15, 30, 35 & 40) is shown in FIG. 1, it is tobe understood that the system (100) can extract data from multipledatabases of each type via the interconnection network (25).

All extracted information concerning revenue, expenses, capital andelements of value is stored in a file or table (hereinafter, table)within an application database (50) as shown in FIG. 2. The applicationdatabase (50) contains tables for storing user input, extractedinformation and system calculations including a system settings table(140), a revenue data table (141), an expense data table (142), acapital data table (143), an equity data table (144), a physical assetID table (145), an asset liquidation price table (146), an accountnumber structure table (147), an equity forecast table (148), a datadictionary table (149), a revenue component definition table (150), anexpense component definition table (151), a capital component definitiontable (152), an element of value definition table (153), a sub-elementdefinition table (154), an enterprise definition table (155), acomposite variable table (156), a sub-element weights table (157), arevenue model gene table (158), a revenue model weights table (159), anexpense model gene table (160), an expense model weights table (161), acapital model gene table (162), a capital model weights table (163), arevenue component percentage table (164), an expense componentpercentage table (165), a capital component percentage table (166), acomposite variable location table (167), a composite variable data table(168), a normalized composite variable data table (169), an enterprisevalue table (170), an economic equity values table (171), a reportstable (172), a tax data table (173), a debt data table (174), a growthoption definition table (175), a growth option overlap table (176), agrowth option scenario table (177), a growth option value table (178), arevenue driver table (179), an expense driver table (180), a capitaldriver table (181), an excluded variables table (182) and a driver genestable (183). The application database (50) can optionally exist as adatamart, data warehouse or departmental warehouse. The system of thepresent invention has the ability to accept and store supplemental orprimary data directly from user input, a data warehouse or otherelectronic files in addition to receiving data from the databasesdescribed previously. The system of the present invention also has theability to complete the necessary calculations without receiving datafrom one or more of the specified databases. However, in the preferredembodiment all required information is obtained from the specifieddatabases (5, 10, 15, 30, 35 & 40).

As shown in FIG. 3, the preferred embodiment of the present invention isa computer system (100) illustratively comprised of a client personalcomputer (110) connected to an application server personal computer(120) via an interconnection network (25). The application serverpersonal computer (120) is in turn connected via the interconnectionnetwork (25) to a database-server personal computer (130).

The database-server personal computer (130) has a CPU (137), a keyboard(133), a CRT display (134), a mouse (136), a printer (138), a hard drive(132) for storage of the basic financial system database (10), theoperation management system database (15), the advanced financial systemdatabase (30), the sales management system database (35) and the humanresource system database (40), a communications bus (135) and aread/write random access memory (131).

The application-server personal computer (120) has a CPU (127), akeyboard (123), a mouse (126), a CRT display (124), a printer (128), ahard drive (122) for storage of the application database (50) and themajority of the application software (200, 300, 400, 500, 600, 700 and800) of the present invention, a communication bus (125) and aread/write random access memory (121). While only one client personalcomputer is shown in FIG. 3, it is to be understood that theapplication-server personal computer (120) can be networked to fifty ormore client personal computers (110) via the interconnection network(25). The application-server personal computer (120) can also benetworked to fifty or more server client personal computers (110) viathe interconnection network (25). It is to be understood that thediagram of FIG. 3 is merely illustrative of one embodiment of thepresent invention.

The client personal computer (110) has a CPU (117), a keyboard (113), amouse (116), a CRT display (114), a printer (118), a modem (119), a harddrive (112) for storage of a client data-base (49) and theuser-interface portion of the application software (900), acommunication bus (115) and a read/write random access memory (111).

The application software (200, 300, 400, 500, 600, 700, 800 and 900)controls the performance of the central processing unit (127) as itcompletes the calculations required to calculate the detailed businessvaluation. In the embodiment illustrated herein, the applicationsoftware program (200, 300, 400, 500, 600, 700, 800 and 900) is writtenin a combination of PowerScript, C++ and Visual Basic®. The applicationsoftware (200, 300, 400, 500, 600, 700, 800 and 900) also usesStructured Query Language (SQL) for extracting data from other databases(10, 15, 30, 35 and 40) and then storing the data in the applicationdatabase (50) or for receiving input from the user (20) and storing itin the client database (49). The other databases contain informationregarding historical financial performance (10), operation managementrecords (15), forecast financial performance (30), sales prospects andtransactions (35) and the company employees (40) that are used in theoperation of the system (100). The user (20) provides the informationthe application software requires to determine which data need to beextracted and transferred from the database-server hard drive (132) viathe interconnection network (25) to the application-server computer harddrive (122) by interacting with user-interface portion of theapplication software (900). The extracted information is combined withinput received from the keyboard (113) or mouse (116) in response toprompts from the user-interface portion of the application software(900) before processing is completed.

User input is initially saved to the client database (49) before beingtransmitted to the communication bus (125) and on to the hard drive(122) of the application-server computer via the interconnection network(25). Following the program instructions of the application software,the central processing unit (127) accesses the extracted data and userinput by retrieving it from the hard drive (122) using the random accessmemory (121) as computation workspace in a manner that is well known.

The computers (110, 120 and 130) shown in FIG. 3 illustratively are IBMPCs or clones or any of the more powerful computers or workstations thatare widely available. Typical memory configurations for client personalcomputers (110) used with the present invention should include at least32 megabytes of semiconductor random access memory (111) and at least a2 gigabyte hard drive (112). Typical memory configurations for theapplication-server personal computer (120) used with the presentinvention should include at least 64 megabytes of semiconductor randomaccess memory (121) and at least a 50 gigabyte hard drive (122). Typicalmemory configurations for the database server personal computer (130)used with the present invention should include at least 128 megabytes ofsemiconductor random access memory (131) and at least a 200 gigabytehard drive (132).

Using the system described above, the value of the enterprise will befurther broken down into tangible and intangible elements of value. Asshown in Table 1, the value of the current-operation will be calculatedusing an income valuation model. An integral part of most incomevaluation models is the calculation of the present value of the expectedcash flows, income or profits associated with the current-operation. Thepresent value of a stream of cash flows is calculated by discounting thecash flows at a rate that reflects the risk associated with realizingthe cash flow. For example, the present value (PV) of a cash flow of tendollars ($10) per year for five (5) years would vary depending on therate used for discounting future cash flows as shown below.

$\begin{matrix}\underset{\_}{{{Discount}\mspace{14mu} {rate}} = {25\%}} \\{{PV} = {{\frac{10}{1.25} + \frac{10}{(1.25)^{2}} + \frac{1}{(1.25)^{3}} + \frac{10}{(1.25)^{4}} + \frac{10}{(1.25)^{5}}} = 26.89}}\end{matrix}$ $\begin{matrix}\underset{\_}{{{Discount}\mspace{14mu} {rate}} = {35\%}} \\{{PV} = {{\frac{10}{1.35} + \frac{10}{(1.35)^{2}} + \frac{1}{(1.35)^{3}} + \frac{10}{(1.35)^{4}} + \frac{10}{(1.35)^{5}}} = 22.20}}\end{matrix}$

The first step in evaluating the elements of current-operation value isseparating the underlying formula that defines the value of thecurrent-operation as shown in Table 3.

TABLE 3 Value of current-operation = (R) Value of expected revenue fromcurrent-operation + (E) Value of expected expense forcurrent-operation + (C) Value of capital required to supportcurrent-operation* *Note: (C) can have a positive or negative valueThe three components of current-operation value will be referred to asthe revenue value (R), the expense value (E) and the capital value (C).Examination of the equation in Table 3 shows that there are three waysto increase the value of the current-operation—increase the revenue,decrease the expense or decrease the capital requirements (note: thisstatement ignores a fourth way to increase value—decrease interest rateused for discounting future cash flows).

While it is possible to break each component down into a large number ofsub-components for analysis, the preferred embodiment has apre-determined number of sub-components for each component of value. Therevenue value is not subdivided. The expense value is subdivided intofive sub-components: the cost of raw materials, the cost of manufactureor delivery of service, the cost of selling, the cost of support and thecost of administration. The capital value is subdivided into sixsub-components: cash, non-cash financial assets, production equipment,other assets (non financial, non production assets), financialliabilities and equity. The production equipment and equitysub-components are not used directly in evaluating the elements ofvalue.

The components and sub-components of current-operation value will beused in calculating the value of the tangible and intangible elements ofvalue. For the calculations completed by the present invention, anelement of value will be defined as “an identifiable entity or groupthat as a result of past transactions has provided and is expected toprovide economic benefit to the enterprise.” An item will be defined asa single member of the group that defines an element of value. Forexample, an individual salesman would be an “item” in the “element ofvalue” sales staff. Predictive models are used to determine thepercentage of: the revenue value, the expense value sub-components, andthe capital value sub-components that are attributable to each elementof value. The resulting values will then be added together to determinethe valuation for different elements as shown by the example in Table 4.

TABLE 4 Valuation of the Large, Loyal Customer Element Revenue value =$120 M 13% attributed to large, Value = $15.6 M loyal customers Expensevalue = ($80 M) 10% attributed to large, Value = ($8) M loyal customersCapital value = ($5 M) 12% attributed to large, Value = ($.6) M loyalcustomers Total value = $35 M Large, Loyal Customer Element Value = $7 M

The valuation of an enterprise using the approach outlined above iscompleted in seven distinct stages. The first stage of processing (block200 from FIG. 1) extracts, aggregates and stores the data from userinput, existing internal databases (10, 15, 30, or 40) and externaldatabases (5) required for the calculation of enterprise business valueas shown in FIG. 5A and FIG. 5B. The second stage of processing (block300 from FIG. 1) identifies the item variables and item performanceindicators that drive the components of value (revenue, expense andchanges in capital) and calculates composite variables that characterizethe performance of the elements of value, as shown in FIG. 6A FIG. 6B,FIG. 6C, FIG. 6D, FIG. 6E, FIG. 6F, FIG. 10 and FIG. 11. The third stageof system processing (block 400 from FIG. 1) values the growth optionsby enterprise using option pricing algorithms as shown in FIG. 7. Thefourth stage of system processing (block 500 from FIG. 1) values therevenue, expense and capital components and calculates the currentoperation value using the information prepared in the previous stage ofprocessing as shown in FIG. 8. The fifth stage of system processing(block 600 from FIG. 1) specifies and optimizes predictive models todetermine the relationship between the value drivers and the revenue,expense and capital values as shown in FIG. 9A, FIG. 9B, FIG. 9C andFIG. 10. The sixth stage of processing (block 700 from FIG. 1) combinesthe results of the fourth and fifth stages of processing to determinethe value of each element as shown in FIG. 12. The seventh and finalstage of processing (block 800 from FIG. 1) determines the relationshipbetween equity and calculated total value as shown in FIG. 13 anddisplays the results of the prior calculations in specified formats asshown in FIG. 14 and FIG. 15.

Extraction and Aggregation of Data

The flow diagrams in FIG. 5A and FIG. 5B detail the processing that iscompleted by the portion of the application software (200) thatextracts, aggregates and stores the information required for systemoperation from: the basic financial system database (10), operationmanagement system database (15), advanced financial system database(30), sales management system database (35), human resource systemdatabase (40), external databases found on the internet (5) and the user(20). A brief overview of the different databases will be presentedbefore reviewing each step of processing completed by this portion (200)of the application software.

Corporate financial software systems are generally divided into twocategories, basic and advanced. Advanced financial systems utilizeinformation from the basic financial systems to perform financialanalysis, financial planning and financial reporting functions.Virtually every commercial enterprise uses some type of basic financialsystem as they are required to use these systems to maintain books andrecords for income tax purposes. An increasingly large percentage ofthese basic financial systems are resident in microcomputer andworkstation systems. Basic financial systems include general-ledgeraccounting systems with associated accounts receivable, accountspayable, capital asset, inventory, invoicing, payroll and purchasingsubsystems. These systems incorporate worksheets, files, tables anddatabases. These databases, tables and files contain information aboutthe company operations and its related accounting transactions. As willbe detailed below, these databases, tables and files are accessed by theapplication software of the present invention as required to extract theinformation required for completing a business valuation. The system isalso capable of extracting the required information from a datawarehouse (or datamart) when the required information has beenpre-loaded into the warehouse.

General ledger accounting systems generally store only valid accountingtransactions. As is well known, valid accounting transactions consist ofa debit component and a credit component where the absolute value of thedebit component is equal to the absolute value of the credit component.The debits and the credits are posted to the separate accountsmaintained within the accounting system. Every basic accounting systemhas several different types of accounts. The effect that the posteddebits and credits have on the different accounts depends on the accounttype as shown in Table 5.

TABLE 5 Account Type Debit Impact Credit Impact Asset Increase DecreaseRevenue Decrease Increase Expense Increase Decrease Liability DecreaseIncrease Equity Decrease IncreaseGeneral ledger accounting systems also require that the asset accountbalances equal the sum of the liability account balances and equityaccount balances at all times.

The general ledger system generally maintains summary, dollar onlytransaction histories and balances for all accounts while the associatedsubsystems, accounts payable, accounts receivable, inventory, invoicing,payroll and purchasing, maintain more detailed historical transactiondata and balances for their respective accounts. It is common practicefor each subsystem to maintain the detailed information shown in Table 6for each transaction.

TABLE 6 Subsystem Detailed Information Accounts Vendor, Item(s),Transaction Date, Amount Owed, Due Payable Date, Account Number AccountsCustomer, Transaction Date, Product Sold, Quantity, Receivable Price,Amount Due, Terms, Due Date, Account Number Capital Asset ID, AssetType, Date of Purchase, Purchase Price, Asset Useful Life, DepreciationSchedule, Salvage Value Inventory Item Number, Transaction Date,Transaction Type, Transaction Qty, Location, Account Number InvoicingCustomer Name, Transaction Date, Item(s) Sold, Amount Due, Due Date,Account Number Payroll Employee Name, Employee Title, Pay Frequency, PayRate, Account Number Purchasing Vendor, Item(s), Purchase Quantity,Purchase Price(s), Due Date, Account NumberAs is well known, the output from a general ledger system includesincome statements, balance sheets and cash flow statements in welldefined formats which assist management in measuring the financialperformance of the firm during the prior periods when data input hasbeen completed.

Advanced financial systems, including financial planning systems,generally use the same format used by basic financial systems inforecasting income statements, balance sheets and cash flow statementsfor future periods. Management uses the output from financial planningsystems to highlight future financial difficulties with a lead timesufficient to permit effective corrective action and to identifyproblems in company operations that may be reducing the profitability ofthe business below desired levels. These systems are most oftendeveloped by individuals within companies using 2 and 3 dimensionalspreadsheets such as Lotus 1-2-3®, Microsoft Excel® and Quattro Pro®. Insome cases, financial planning systems are built within an executiveinformation system (EIS) or decision support system (DSS). For thepreferred embodiment of the present invention, the advanced financialsystem database is the financial planning system database detailed inU.S. Pat. No. 5,165,109 for “Method of and System for GeneratingFeasible, Profit Maximizing Requisition Sets”, by Jeff S. Eder, thedisclosure of which is incorporated herein by reference.

While advanced financial systems are similar between firms, operationmanagement systems vary widely depending on the type of company they aresupporting. These systems typically have the ability to not only trackhistorical transactions but to forecast future performance. Formanufacturing firms, operation management systems such as EnterpriseRequirements Planning Systems (ERP), Material Requirement PlanningSystems (MRP), Purchasing Systems, Scheduling Systems and QualityControl Systems are used to monitor, coordinate, track and plan thetransformation of materials and labor into products. These systems willgenerally track information about the performance of the differentvendors that supply materials to the firm including the informationshown in Table 7.

TABLE 7 Operation Management System - Vendor Information 1. Vendor Name2. Vendor Number 3. Commodity Code(s) 4. Year to date dollar volume 5.Historical dollar volume 6. Percentage of deliveries rejected by QC 7.Percentage of deliveries accepted out of specification 8. Compliancewith ISO 9000 9. Actual lead time required for purchases 10. Terms andconditions for purchases 11. Average Delivery Quantity Variance 12.Average Delivery Date Variance 13. EDI* vendor - Yes or No *EDI =Electronic Data InterchangeSystems similar to the one described above may also be useful fordistributors to use in monitoring the flow of products from amanufacturer.

Operation Management Systems in manufacturing firms may also monitorinformation relating to the production rates and the performance ofindividual production workers, production lines, work centers,production teams and pieces of production equipment including theinformation shown in Table 8.

TABLE 8 Operation Management System - Production Information  1. IDnumber (employee id/machine id)  2. Actual hours - last batch  3.Standard hours - last batch  4. Actual hours - year to date  5.Actual/Standard hours - year to date %  6. Actual setup time - lastbatch  7. Standard setup time - last batch  8. Actual setup hours - yearto date  9. Actual/Standard setup hrs - yr to date % 10. Cumulativetraining time 11. Job(s) certifications 12. Actual scrap - last batch13. Scrap allowance - last batch 14. Actual scrap/allowance - year todate 15. Rework time/unit last batch 16. Rework time/unit year to date17. QC rejection rate - batch 18. QC rejection rate - year to dateOperation management systems are also useful for tracking requests forservice to repair equipment in the field or in a centralized repairfacility. Such systems generally store information similar to that shownbelow in Table 9.

TABLE 9 Operation Management System - Service Call Information  1.Customer name  2. Customer number  3. Contract number  4. Service callnumber  5. Time call received  6. Product(s) being fixed  7. Serialnumber of equipment  8. Name of person placing call  9. Name of personaccepting call 10. Promised response time 11. Promised type of response12. Time person dispatched to call 13. Name of person handling call 14.Time of arrival on site 15. Time of repair completion 16. Actualresponse type 17. Part(s) replaced 18. Part(s) repaired 19. 2nd callrequired 20. 2nd call number

Sales management systems are similar to operation management systems inthat they vary considerably depending on the type of firm they aresupporting and they generally have the ability to forecast future eventsas well as track historical occurrences. In firms that sell customizedproducts, the sales management system is generally integrated with anestimating system that tracks the flow of estimates into quotations,orders and eventually bills of lading and invoices. In other firms thatsell more standardized products, sales management systems generally areused to track the sales process from lead generation to leadqualification to sales call to proposal to acceptance (or rejection) anddelivery. All sales management systems would be expected to storeinformation similar to that shown below in Table 10.

TABLE 10 Sales Management System - Information  1. Customer/Potentialcustomer name  2. Customer number  3. Address  4. Phone number  5.Source of lead  6. Date of first purchase  7. Date of last purchase  8.Last sales call/contact  9. Sales call history 10. Sales contact history11. Sales history: product/qty/price 12. Quotations: product/qty/price13. Custom product percentage 14. Payment history 15. Current A/Rbalance 16. Average days to pay

Computer based human resource systems are increasingly used for storingand maintaining corporate records concerning active employees in sales,operations and the other functional specialties that exist within amodern corporation. Storing records in a centralized system facilitatestimely, accurate reporting of overall manpower statistics to thecorporate management groups and the various government agencies thatrequire periodic updates. In some cases human resource systems includethe company payroll system as a subsystem. In the preferred embodimentof the present invention, the payroll system is part of the basicfinancial system. These systems can also be used for detailed planningregarding future manpower requirements. Human resource systems typicallyincorporate worksheets, files, tables and databases that containinformation about the current and future employees. As will be detailedbelow, these databases, tables and files are accessed by the applicationsoftware of the present invention as required to extract the informationrequired for completing a business valuation. It is common practice forhuman resource systems to store the information shown in Table 11 foreach employee.

TABLE 11 Human Resource System Information  1. Employee name  2. Jobtitle  3. Job code  4. Rating  5. Division  6. Department  7. EmployeeNo./(Social Security Number)  8. Year to date - hours paid  9. Year todate - hours worked 10. Employee start date - company 11. Employee startdate - department 12. Employee start date - current job 13. Trainingcourses completed 14. Cumulative training expenditures 15. Salaryhistory 16. Current salary 17. Educational background 18. Currentsupervisor

External databases such as those found on the internet (5) can be usedfor obtaining information that enables the categorization and valuationof assets such as brand names, trademarks and service marks(hereinafter, referred to as brand names). In some cases it can also beused to supplement information obtained from the other databases (10,15, 30, 35 and 40) that are used in categorizing and evaluating employeegroups and other elements of value. In the system of the presentinvention, the retrieval of information from the internet (5) can be:

-   -   a) targeted to specific on-line publications that provide        information relevant to the element being evaluated,    -   b) restricted to a simple count of the number of matches a        specific trademark generates when entered into a general purpose        internet search-engine such as Yahoo!, Lycos, AltaVista or        HotBot, or WebCrawler, and    -   c) specific searches using commercially available software        agents and/or text mining products to determine both the number        and the type of references (favorable, unfavorable or        information only) that have been made concerning a specific        trademark in all discovered references.

System processing of the information from the different databases (5,10, 15, 30, 35 and 40) described above starts in a block 201, FIG. 5A,which immediately passes processing to a software block 202. Thesoftware in block 202 prompts the user via the system settings datawindow (901) to provide system setting information. The system settinginformation entered by the user (20) is transmitted via theinterconnection network (25) back to the application server (120) whereit is stored in the system settings table (140) in the applicationdatabase (50) in a manner that is well known. The specific inputs theuser (20) is asked to provide at this point in processing are shown inTable 12.

TABLE 12 System Settings  1. Mode of operation - stand-alone valuationor comparison to previous valuation  2. Date of business valuationcalculation (valuation date)  3. Date of previous valuation (if any)  4.Location (address) of basic financial system data dictionary and data 5. Location (address) of advanced financial system data dictionary anddata  6. Location (address) of human resource information system datadictionary and data  7. Location (address) of operation managementsystem data dictionary and data  8. Location (address) of salesmanagement system data dictionary and data  9. Location (address) of anyexternal databases used in the valuation calculation 10. The maximumacceptable age of a valuation (in days) 11. The maximum number ofgenerations to be processed without improving fitness 12. Base currency13. Currency conversions for any non-base currencies used in thefinancial systems 14. Weighted average cost of capital (to be used indiscounting cash flows) 15. Simplified analysis (no sub-components ofexpense or capital value) 16. Number of months a product is considerednew after it is first produced 17. Define composite variables? (Yes orNo) 18. Amount of cash and marketable securities required for day to dayoperationsThe application of these system settings will be explained as part ofthe detailed explanation of the system operation.

The software in block 202 uses the valuation date specified by the user(20) to determine the time periods (months) that require data in orderto complete the valuation of the current operation and the growthoptions and stores the resulting date range in the system settings table(140). The valuation of the current operation by the system requiressales, operation, finance, external database and human resource data forthe three year period before and the four year period after thespecified valuation date. Because of the difficulties inherent inforecasting from the perspective of the past or the future, thespecified valuation date is generally within a month of the currentsystem date. After the storage of system setting data is complete,processing advances to a software block 203 where the data dictionariesfrom the basic financial system database (10), the operation managementsystem database (15), the advanced financial system database (30), thesales management system database (35) and the human resource systemdatabase (40) are extracted and saved in the data dictionary table (149)in the application database (50) and processing advances to a softwareblock 204. The software in block 204 checks the system settings table(140) in the application database (50) to determine if the currentcalculation is a comparison to a prior valuation or if it is astand-alone calculation. If the calculation involves a comparison with aprior valuation, then the software in block 204 retrieves the previouslydefined account structure, data definitions, enterprise definitions andcomponent definitions and saves them in the appropriate tables for usein the current calculation before processing advances to a softwareblock 209. Alternatively if the calculation is a stand alone, thenprocessing advances to a software block 205.

The software in block 205 interacts with an account structure and datadictionary data window (902) that prompts the user for any input that isrequired to define data fields for the extracted data dictionaries andthe data dictionary of the application software of the presentinvention. This input is also saved to the data dictionary table (149).The software in block 205 also prompts the user (20) via the accountstructure and data dictionary data window (902) for information thatedits or defines the account structure used in the financial systemdatabases. It is common practice for account numbers to have severalsegments where each segment represents a different set of subgroups asshown below in Table 13.

TABLE 13 Account Number 01- 800- 901- 677- 003 Segment Company DivisionDepartment Account Sub- account Subgroup Products Workstation MarketingLabor P.R. Position 5 4 3 2 1

As will be detailed below, the different account number segments areused for separating the financial information for analysis.

After the account structure information is stored in the account numberstructure table (147) in the application database (50), processingadvances to a block 206 where the software in the block interacts withan enterprise definition data window (903) to prompt the user (20) tospecify the account number segment or segments that will be used todefine the enterprise being valued by the innovative system of thepresent invention. For example, the user (20) could specify that eachdivision is to be analyzed as a separate enterprise. In this case, ifthe total company had two business units with six divisions, then theuser could specify up to six enterprises as shown in Table 14.

TABLE 14 Products Business Unit Software Business Unit 1. PC Division 5.Application Software Division 2. Workstation Division 6. OperatingSystem Software Division 3. Mainframe Division 4. Peripherals Division

The specified enterprises are then displayed to the user (20) by thesoftware in block 206 via the enterprise definition data window (903).At this point, the user (20) is given the option of combining theenterprises or leaving them in the initial configuration. For example,the user (20) could combine the Personal Computer Product enterprise andthe Workstation Product enterprise into one enterprise for the businessvaluation calculation. When the user (20) indicates that all enterpriseshave been defined, the resulting specifications are stored in theenterprise definition table (155) in the application database (50).

After the enterprise definitions are stored, processing advances to asoftware block 207 where the software in the block prompts the user (20)via a component definition data window (904) to specify the accountsegment or segments that will be used to define the expense and capitalsub-components for each enterprise. Only account segments with positionnumbers below those of the segment used for enterprise specification canbe used for expense and capital sub-component specification. Continuingthe example shown above for a valuation calculation, departments,accounts and sub-accounts are the only segments that can be utilized forexpense or capital component and sub-component specification. Thislimitation is applicable because their position numbers 3, 2 and 1respectively are below 4, the position number of the division segmentthat was the lowest position used to define the enterprise. As discussedpreviously, there is only one revenue component per enterprise;therefore, the enterprise definition automatically defines the revenuecomponent.

For the normal analysis, each enterprise has: one revenue component,five expense sub-components (cost of raw materials, the cost ofmanufacture or delivery of service, the cost of sales, the cost ofsupport and other costs), four capital sub-components used in thevaluation calculation (cash, non-cash financial assets, other(non-financial, non-production) assets and liabilities), and two capitalsub-components that are not used directly in the valuation calculation(production equipment and equity). The software in block 207 via thecomponent definition data window (904) will accept all logicalcombinations of account number segment specifications for asub-component while preventing the reuse of the same segment for morethan one sub-component specification in each enterprise. Sub-componentdefinitions are required even if the user (20) has chosen to run asimplified analysis (i.e., one without sub-components). Table 15provides examples of expense and capital sub-component definitions.

TABLE 15 Sub-component Definition Expense: Cost of materials Departments10-18, accounts 500 to 505 Expense: Cost of manufacturing Departments10-18, accounts 506 to 999 Expense: Cost of sales Department 21,accounts 500 to 999 Capital: Cash Account 100, all departments Capital:Liabilities Accounts 200-299, all departmentsThe software in block 207 saves the new or updated revenue componentdefinitions to the revenue component definition table (150), expensesub-component definitions to the expense component definition table(151) and capital sub-component definitions to the capital componentdefinition table (152). The production equipment and other assetdefinitions are also used to populate the physical asset ID table (145)and the asset liquidation price table (146) with the names of all assetsused by all enterprises.

After the definitions for the revenue, expense and capital componentshave been stored in the application database (50), processing advancesto a software block 209. Processing can also advance to block 209directly from block 204 if the calculation is a comparison to a priorvaluation. The software in block 209 checks to determine if all theavailable financial data have been included in a revenue, expense, orcapital component or sub-component. In the example shown above, block209 would check to determine that the financial data for all divisions,departments, account numbers and sub-account numbers have been assignedto a component. If the software in block 209 determines that allfinancial data have been assigned to a component, then processingadvances to a software block 210. Alternatively, if the software inblock 209 determines that some financial data have not been assigned toa component, then processing advances to a software block 208. Thesoftware in block 208 prompts an edit component definition data window(905) to display a screen that provides the user (20) with the abilityto redefine previously stored component and sub-component definitions toinclude the unassigned financial data. The revised componentdefinition(s) are then saved in the appropriate definition table(s)(150, 151 or 152) in the application database (50) and processingreturns to block 209 and from there to software block 210.

The software in block 210 retrieves the debit or credit balances fromthe basic financial system database (10) and the advanced financialsystem database (30) in account segment position order, lowest positionto highest position, for the revenue, expense and capital components forthe time periods determined by the software in block 202 and stored inthe system settings table (140). Continuing the example, the software inblock 210 would first retrieve and total the debits and credits in eachrequired period for the sub-components that have sub-accountspecifications. The higher level specifications, account number,department and division, are observed when data are retrieved for thesub-components with sub-account specifications. The software in block210 would then retrieve the required data for the sub-components withaccount number specifications. The higher level specifications,department and division, are observed when data are retrieved for thesub-components with account number specifications. The software in block210 would finally retrieve the required data for the sub-components withdepartment number specifications. The higher level specification,division, is observed when data are retrieved for these sub-components.This same procedure is completed for each enterprise and the resultingtotals are then saved in the appropriate data tables (141-revenue,142-expense and 143-capital) in the application database (50).

After all the financial data have been extracted and stored in theapplication database (50), system processing advances to a softwareblock 212. The software in block 212 determines if any of the componentsor sub-components are missing data for any of the required periods.Missing data is defined as the condition when there is a null value fora sub-component financial data field in a required period. If thesoftware in block 212 determines that all components have the requireddata in all periods, then processing advances directly to a softwareblock 221. Alternatively, if data are missing, then processing advancesto a software block 213 where the user (20) is prompted by a missingfinancial data window (906) to provide the missing data or the locationof the missing data. In some cases the user (20) may simply replace thenull value with a zero. After the user (20) provides the missing data orthe location of the missing data, the appropriate data tables(141-revenue, 142-expense and/or 143-capital) in the applicationdatabase (50) are updated and processing advances to software block 221.

The next step in system processing is completed by software block 221where the software in the block prompts the user (20) via an element ofvalue specification data window (907) to define the elements of valuefor each enterprise, to indicate the maximum number of sub-elements foreach element and to identify the identity and location of transactiondata and other information that are related to each element of value.Elements of value with sample specifications are shown below in Table16.

TABLE 16 Element of Value: Maximum Name/ Sub Element of Value DataDefinition Elements Identity and Location Customers/ 10 Account paymentdata (10); Communications Customer numbers history (15), Date of firstorder (35); Order history - 1-21,877 line items, products/services,revenue, returns, delivery (10 & 35); Invoice adjustment history (10 &35), Service call history - first time and repeat (15); Technicalsupport call history - first time and repeat (15). Employees Production/ 0 Date of first employment (40), Employee Job codes: suggestion history(15); Employee training data 17, 18, 19 and 33 (40); Employee productiondata - hours, piece quantity (15); Employee pay data including benefits(10, 30 & 40). Brand names/  50* Monthly average pricepremium/(discount) vs. Name(s) industry average price (35), Monthlynumber of favorable mentions in trade press (5), Monthly number of hitson corporate web site (5), Monthly spending on advertising (10), Monthlyaverage cost per 1,000 for advertising (10). *Default system limitThe information entered by the user (20) defining the elements of valueis stored in the element of value definition table (153), the locationof the element of value data is stored in the composite variablelocation table (167), and an index of the element of value data isstored in the composite variable data table (168) in the applicationdatabase (50), before processing advances to a software block 222.

The software in block 222 prompts the user (20) via a growth optiondefinition data window (908) to specify the growth options that will bevalued for each enterprise. The specification of each growth optionincludes: an option name, the financial resources consumed or generatedby the growth option by component of value, the resources associatedwith the growth option by element of value, and the number of scenariosthat will be analyzed as part of the growth option valuation. A growthoption specification example is shown below in Table 17.

TABLE 17 Growth Option Example Specification Option name VRML EquipmentRevenue Component None Expense Sub-Component: Department 17, accounts500 to 505, Raw Materials after 6/97 Expense Sub-Component: Allexpenses, department 87 Other Capital Sub-Component: All assets,department 87 Other Assets Element of Value: All employees, department87 Other EmployeesIf the system (100) is calculating a business valuation comparison, thenthe input from the user (20) regarding growth options is limited todefining new growth options. After the user's input is stored in thegrowth option definition table (175) in the application database (50),processing advances to a software block 223. The software in block 223retrieves data from the different databases in accordance with thespecifications provided by the user (20) in the previous two steps.After this information is stored in the application database (50),processing advances to a software block 225.

The software in block 225 prompts the user (20) via a tax informationdata window (910) to provide an overall tax rate for the company anddetailed schedules for federal income taxes plus any other taxes asshown in Table 18.

TABLE 18 Tax Example Schedule Federal Income Tax   15% of first $250,000in profit   25% of next $500,000 in profit   35% of profit over $750,000State Tax 2.25% of revenue Overall Tax Rate   33% of GAAP operatingprofitAfter the information the user (20) provides is stored in the tax datatable (173) in the application database (50), processing advances to asoftware block 226. The software in block 226 prompts the user (20) viaan equity information data window (911) to provide historical andforecast (Fcst) information for each account included in the equitysub-component specification stored in the capital component definitiontable (152) as shown in Table 19.

TABLE 19 Actual/ Equity Account Example Schedule Fcst 301 - Preferredstock 100,000 shares @ $40/share A Sep. 1, 1987 with yield 5% 250,000shares @ $90/share F Mar. 31, 1998 with yield 8% 302 - Common Stock1,000,000 shares @ $20/share on A valuation date Price history for last5 years A 303 - Dividends Actual dividends last 5 years AAfter the information the user (20) provides is stored in the equitydata table (144) in the application database (50), processing advancesto a software block 227.

The software in block 227 prompts the user (20) via a liabilityinformation data window (912) to provide historical and forecastinformation concerning each account included in the financial liabilitysub-component stored in the capital component definition table (152) asshown in Table 20.

TABLE 20 Actual/ Liability Account Example Schedule Fcst 201 - AccountsNA Payable 203 - Accrued NA Salary 205 - Short Term   $150,000 @ 12%annual, Dec. 31, 1991 A Debt   $250,000 @ 11.7% annual, Mar. 17, 1993 A  $250,000 @ 11% annual, Jun. 30, 1999 F 215 - Long Term $2,500,000 @8.5% annual, Sep. 1, 1993 A DebtAfter the information the user (20) provides is stored in the debt datatable (174) in the application database (50), processing advances to asoftware block 228.

The software in block 228 calculates the current weighted average costof capital using the information stored in the debt and equity tables(174 and 144, respectively) using Formula 1 shown below.

Weighted average cost of capital=((D/V)×R_(D))(1−T)+(E/V×R_(E)  Formula1

Where:

D=Value of Debt, E=Value of Equity, R_(D)=Weighted Average Interest Rateof Debt, T=Tax Rate, R_(E)=Rate of Return on Equity (based on historicalinformation provided) and V=(D+E)

After the calculation is completed, processing advances to a softwareblock 229. The software in block 229 compares the calculated value tothe value previously specified by the user (20) in the system settingstable (140). If the two values are different, then processing advancesto a software block 230 which prompts the user via a cost of capitalselection data window (913) to select the cost of capital figure to usefor future calculations. The cost of capital specified by the user (20)is stored in the system settings table (140) and processing returns toblock 229 and on to a software block 232. System processing passesdirectly to block 232 if the calculated and specified values of the costof capital are identical.

The software in block 232 checks the asset liquidation price table (146)to determine if there are “current” (as defined previously) liquidationprices for all physical assets listed in the physical asset ID table(145). If there are “current” prices for all physical assets listed inthe physical asset ID table (145), then processing advances to asoftware block 302 where the identification of the value drivers begins.If, on the other hand, there are not “current” prices for all physicalassets, then processing advances to a software block 235. The softwarein block 235 prompts the user (20) via a liquidation price entry datawindow (914) to provide liquidation prices for all physical assets thatdon't have “current” values. The user (20) is given the option ofspecifying a liquidation value as a fixed price, as a percentage oforiginal purchase price or as a percentage of book value (as stored inthe basic financial system database (10)). After the requiredinformation has been entered by the user (20) and stored in the assetliquidation price table (146) in the application database (50), systemprocessing advances to a software block 302.

Identify Value Drivers by Element

The flow diagrams in FIG. 6A, FIG. 6B, FIG. 6C, FIG. 6D, FIG. 6E andFIG. 6F detail the processing that is completed by the portion of theapplication software (300) that identifies the item variables and itemperformance indicators that drive revenue, expense and changes incapital by element for all defined enterprises. The item variables anditem performance indicators identified during this processing arecollectively referred to as “value drivers”.

Processing begins in software block 302. The software in block 302checks the composite variable table (156) and the revenue driver table(179) in the application database (50) to determine if all enterpriserevenue components have “current” drivers and composite variables forall elements. If all enterprise revenue components have “current”drivers for all elements, then processing advances to a software block306. Alternatively, if there are any revenue components without“current” drivers for at least one element, then processing advances toa software block 303. The software in block 303 uses the element ofvalue definition table (153) and excluded variables table (182) to guidethe retrieval of information required to specify the next revenue drivermodel that is being updated. All information related to the enterpriseelement being examined less any information identified in the excludedvariables table (182) is retrieved by block 303 from the primarydatabases including: the basic financial system database (10), theoperation management system database (15), the advanced financial systemdatabase (30), the sales management system database (35), the humanresource system database (40), and external databases found on theinternet (5) by item. For example, if the element being modeled was thecustomer element that was defined by the customer numbers in the rangefrom 1 to 21,877, then all numeric and date fields in data recordscontaining a customer number, save those listed in the excludedvariables table (182), would be retrieved and stored in the revenuedriver table (179) by item. The numeric and date field data arecollectively referred to as “item variables”. When all item variableshave been stored in the revenue driver table (179), processing advancesto a software block 304.

The software in block 304 calculates expressions by item for eachnumeric data field including: summary data like cumulative total value;ratios like the period to period rate of change in value; trends likethe rolling average value and time lagged data like the time laggedvalue of each numeric item variable. In a similar fashion the softwarein block 304 calculates expressions for each date field includingsummary data like time since last occurrence and cumulative time sincefirst occurrence; and trends like average frequency of occurrence andthe rolling average frequency of occurrence. The numbers calculated fromnumeric and date fields are collectively referred to as “itemperformance indicators”. After the item performance indicators arecalculated and stored in the revenue driver table (179) in theapplication database (50), processing advances to a software block 305.

The software in block 305 creates a predictive time series neural netmodel for the revenue driver. More specifically, the software in theblock creates a neural network model that relates the item variables anditem performance indicators for a given enterprise to the revenuecomponent. Neural networks are increasingly being used for statisticallymodeling the relationships between sets of data. One of the main reasonsfor the increase in their use is that they are effective in modelingrelationships even when there are nonlinear relationships andinteractions between independent variables. Neural networks consist of anumber of processing elements (hereinafter, referred to as nodes) thatsend data to one another via connections. The strengths of theconnections between the nodes are referred to as weights. As shown inFIG. 10, there are three types of nodes, input nodes (710-x), hiddennodes (720-x) and output nodes (730). Input nodes receive data valuesfrom input variables (701). A threshold node (710-THRESH) is a specialclass of input node (710-x) with a constant weight of 1 on theconnection to a hidden node (720-x). Hidden nodes (720-x) createintermediate representations of the relationship between input data andthe output values. It is important to note that while the diagram inFIG. 10 shows only one layer of hidden nodes (703), in many cases anetwork model will contain several layers of hidden nodes. Finally,output nodes (730) produce output variables (705).

The action of a neural network is determined by two things: thearchitecture, that is how many input, hidden and output nodes it has;and the values of the weights. A neural network “learns” by modifyingits weights (706 and 707) to minimize the difference between thecalculated output value (705) and the actual output value. Thedifference between the calculated output value and the actual outputvalue is defined as the error function for the network. For revenuecomponents such as those specified by the software in block 305, theerror function is defined by Formula 2.

Formula 2 ERR (W)_(k) = 1/2 (R_(k) − Y(W))² Where: W = a set of weightvalues ERR(W)_(k) = error function for W for period k R_(k) =actual/forecast revenue for period k Y(W) = output value for WThe process for minimizing the error function will be detailed after thespecification of the network architecture is explained.

The software in block 305 determines the number of the input nodes andhidden nodes for each network as a function of the number of itemvariables and item performance indicators specified by the software inblocks 303 and 304. There are also additional input nodes for priorperiod revenue and for a threshold node. For the system of the presentinvention, the number of hidden nodes is derived by adding one (1) tothe number of input nodes. Table 21 shows the calculation of the numberof nodes in an example predictive revenue model.

TABLE 21 Potential Value Drivers - Element to Revenue Model by ItemQuantity Item Variables 6 Item Performance Indicators 48 Subtotal InputNodes: 54 Threshold & Prior Period Nodes 2 Total Input Nodes: 56 HiddenNode Adder 1 Total Hidden Nodes: 57The software in block 305 sets the initial number of hidden layers toone. The software in block 305 also establishes one output node for therevenue and sets all weights to random numbers between 0 and 1 (exceptthe threshold node weight which is fixed at 1).

The processing completed by all of the network nodes (710-x, 720-x and730) is similar. The input nodes (710-x) receive their input of itemvariables and item performance indicators by item by period while thehidden node (720-x) receives its input from the input nodes and theoutput nodes (730-x) receive their input from the hidden nodes. Eachnode multiplies the received input by the corresponding weight (706 or707) to produce a weighted sum. The network applies a sigmoid or linearfunction to the weighted sum to determine the state of the node. Thestate of each node is then passed on to the next layer along a weightedconnection or it is used to generate an output variable. When thenetwork architecture including the nodes has been specified by thesoftware in block 305, then processing advances to a software block 325where network optimization begins.

The normal operation of a neural network requires the use of very largeamounts of data to train the network to minimize the error function andthen test the networks predictive capabilities. The preferred embodimentof the present invention minimizes the need for very large data sets byusing genetic algorithms to find the weights (W) that reduce the errorfunction to an acceptable level before optimizing the network using thebackpropagation algorithm to determine the “best fit”. The software in ablock 325 uses genetic algorithms to find solutions for the currenterror minimization problem by evolving a set of solutions toward thedesired goal of having an error function value of zero. Morespecifically, the genetic algorithms in block 325 create and maintain apopulation of the software equivalent of DNA chromosomes (hereinafter,chromosomes) that “evolve” toward the specified goal by using selectivecrossover and random mutation to generate new chromosomes. For thisapplication, the chromosomes (see Table 22 below) encode the networkweights.

TABLE 22

Each individual “gene” represents a weight between two sets of nodes.The fitness of each chromosome in the population is evaluated by theproximity of the resulting solution to the expected objective functionmaximum (the maximum of the objective function corresponds to theminimum error level of the neural network). Selective crossover in agenetic algorithm gives a preference to the chromosomes (sets ofweights) that are the most fit (e.g., have lowest error and highestobjective function outputs). Crossover is a form of reproduction thatseparates each of two individual chromosomes into two separate pieces ata random break point. Crossover is completed when the algorithmrecombines the top piece from the first chromosome with the bottom pieceof the second chromosome and the bottom piece from the first chromosomewith the top piece from the second chromosome to produce two newchromosomes that have a mix of “genes” from each of the originalchromosomes. Giving a preference to the most fit chromosomes increasesthe likelihood that the new chromosomes will produce more fit solutionsthan the precursor chromosomes. Mutation is the random change in thevalue of a randomly selected “gene”. Mutation occurs to “genes” duringcrossover. It also occurs in individual chromosomes within thepopulation. When a population of chromosomes has been crossed over andmutated, a new generation of the population is created. The fitness ofthe chromosomes within the new population is evaluated and unless one ofthe chromosomes produces an acceptable solution (a solution where theerror level is below the target), the process is repeated. Over time theselective crossover will increase the relative fitness of the populationand decrease the difference between the best and worst chromosomes.

The evolutionary process is enhanced in the present invention usingthree separate mechanisms. First, the fitness measures for individualchromosomes are re-scaled before crossover by the software in block 325whenever the difference between the fitness of the top 10% of populationand the bottom 10% of the population is less than 5% of the expectedsolution. To accomplish this, the fitness of the chromosome(s) with thelowest fitness is arbitrarily changed to 10% of the target value and thefitness of the chromosome(s) with the highest fitness is set to 95% ofthe target value. The remaining chromosomes fitness values are adjustedaccordingly. This adjustment has the effect of restoring the relativeadvantage that the fitter chromosomes have in being selected forcrossover.

The second mechanism for speeding the evolutionary process is to pickonly the fittest members of a population for inclusion in the nextgeneration. For this procedure, the current generation is combined withthe two preceding generations and the fittest third from the combinedpopulation is carried forward for crossover and mutation in the nextgeneration by the software in block 325. Finally, the sensitivity of thesolution to the inclusion of all “genes” is tested when the fitness of achromosome reaches the target level or the fitness of the populationfails to increase for the maximum number of successive generationsspecified by the user (see System Settings, Table 12). The highest levelof fitness achieved is established as the new target and processingadvances to a block 330 after the resulting genes are stored in thedriver genes table (183). The software in block 330 creates parallelpopulations where the “genes” (weights) associated with one itemvariable or item-performance indicator are removed from each chromosomebefore processing advances to a software block 335.

The software in a block 335 repeats the evolution process using theparallel population with the highest initial average fitness. If thefitness level of a chromosome in the parallel populations equals orexceeds the target value after a minimum number of generations (equal tothe user specified maximum—see System Settings, Table 12) or the fitnessof the population fails to increase for the user specified maximumnumber of successive generations, then processing advances to a block340. If the software in block 340 determines that a chromosome in theparallel population has reached a new target level, then the genes arestored in the driver genes table (183) and the processing returns to ablock 330 where process of creating parallel populations by removingpotential driver “genes” is repeated. The overall process of evolutionand removal of item variables and item performance indicators continuesin this manner until the new parallel populations fail to reach a newtarget level at which point processing is advanced to a software block345.

The software in block 345 uses the three chromosomes that achieved thehighest fitness to initialize three distinct induction algorithms orcausal models. While the neural network software in blocks 325 and 335is capable of determining which item variables and item performanceindicators correlate most strongly with changes in revenue, theirconfiguration does not provide for identification of the item variablesand item performance indicators that are causing changes in revenue(i.e. the “value drivers”). The item variables and item performanceindicators that didn't correlate strongly with changes in revenue were“pruned” during the evolution of the high fitness chromosomes. As aresult, the chromosomes in the three most “fit” chromosomes contain theitem variables and item performance indicators that correlate moststrongly with revenue changes. Eliminating low correlation factors fromthe initial configuration of the induction algorithms increases theirprocessing efficiency.

A brief description of the three algorithms initialized by the softwarein block 345 are presented below in Table 23.

TABLE 23 Induction Algorithm Description Entropy Starting with nothing,add variables to composite Minimization variable formula as long as theyincrease the explainability of result. LaGrange Algorithm designed toidentify the behavior of dynamic systems uses linear regression of thetime derivatives of the system variables. Path Essentially equivalent tomultiple linear regression that Analysis finds the least squares rulefor more than one predictor variable.In addition to identifying the value drivers, these algorithms produceformulas that summarize the performance of the element being examined incausing changes in revenue.

After the models are initialized by the software in block 345,processing passes to a software block 350. The software in block 350sub-divides the item variable, item performance indicator and revenuedata into ten (10) distinct subgroups before processing passes to ablock 355. The software in block 355 uses a model selection algorithm toidentify the induction algorithm that best fits the data for the elementbeing examined. For the system of the present invention, a crossvalidation algorithm is used for model selection. The software in block355 optimizes each of the induction algorithms using nine (9) of the ten(10) sets of data. As part of this processing, the duplication of theinformation related to each item is eliminated as only the strongestcausal factor variables are included in the final solution. Theresulting equation from each induction algorithm is then tested usingthe data from the remaining set to identify the causal model thatproduces the best fit for that set of test data. The equations producedby the induction algorithms will hereinafter be referred to as compositevariables. This process is repeated ten (10) times which allows eachsubgroup to be used as the basis for validating model performance. Thecomposite variables and value drivers from the induction algorithm thatproduced the best results are then saved in the composite variable table(156), composite variable data table (168) and revenue driver table(179) in the application database (50) and processing returns to a block302.

If the software in block 302 determines that there are elements thatstill require new revenue value driver models, then the processingdescribed in the preceding paragraphs is repeated. Alternatively, if thesoftware in block 302 determines that there are “current” revenue valuedrivers for all elements in all enterprises, then processing advances toa software block 306. The software in block 306 checks the compositevariable table (156) and the expense driver table (180) in theapplication database (50) to determine if all enterprise expensecomponents have “current” drivers and composite variables for allelements. If all enterprise expense components have “current” driversand composite variables for all elements, then processing advances to asoftware block 312. Alternatively, if there are expense componentswithout “current” drivers or composite variables for at least oneelement, then processing advances to a software block 307. The softwarein block 307 uses the element of value definition table (153) andexcluded variables table (182) to guide the retrieval of informationrequired to specify the next expense driver model that is being updated.All information related to the enterprise element being examined lessany information identified in the excluded variables table (182) isretrieved by block 307 from the primary databases including: the basicfinancial system database (10), the operation management system database(15), the advanced financial system database (30), the sales managementsystem database (35), the human resource system database (40), andexternal databases found on the internet (5) by item. When all itemvariables have been stored in the expense driver table (180), processingadvances to a software block 308.

The software in block 308 calculates expressions by item for eachnumeric data field and each date field in manner identical to thatdescribed previously for software block 304. After the item performanceindicators are calculated and stored in the expense driver table (180)in the application database (50), processing advances to a softwareblock 309. The software in block 309 creates a predictive time seriesneural net model for the expense driver in a manner similar to thatdescribed previously for block 305. After the expense value driverpredictive model has been specified, processing proceeds through blocks325, 330, 335, 340, 345, 350 and 355 in a manner identical to thatdescribed above for the processing of the revenue value driver modelbefore returning to block 306.

If the software in block 306 determines that there are elements thatstill require new expense value driver models, than the processingdescribed in the preceding paragraphs is repeated. Alternatively, if thesoftware in block 306 determines that there are “current” expense valuedrivers for all elements in all enterprises, then processing advances toa software block 312. The software in block 312 checks the compositevariable table (156) and the capital driver table (181) in theapplication database (50) to determine if all enterprise capitalcomponents have “current” drivers and composite variables for allelements. If all enterprise capital components have “current” driversand composite variables for all elements, then processing advances to asoftware block 317. Alternatively, if there are capital componentswithout “current” drivers or composite variables for at least oneelement, then processing advances to a software block 313. The softwarein block 313 uses the element of value definition table (153) andexcluded variables table (182) to guide the retrieval of informationrequired to specify the next capital driver model that is being updated.All information related to the enterprise element being examined lessany information identified in the excluded variables table (182) isretrieved by block 313 from the primary databases including: the basicfinancial system database (10), the operation management system database(15), the advanced financial system database (30), the sales managementsystem database (35), the human resource system database (40), andexternal databases found on the internet (5) by item. When all itemvariables have been stored in the capital driver table (181), processingadvances to a software block 314.

The software in block 314 calculates expressions by item for eachnumeric data field and each date field in manner identical to thatdescribed previously for software blocks 304 and 308. After the itemperformance indicators are calculated and stored in the capital drivertable (181) in the application database (50), processing advances to asoftware block 315. The software in block 315 creates a predictive timeseries neural net model for the capital driver in a manner similar tothat described previously for block 305 and 309. After the capital valuedriver predictive model has been specified, processing proceeds throughblocks 325, 330, 335, 340, 345, 350 and 355 in a manner identical tothat described above for the processing of the expense and revenue valuedriver models before returning to block 312.

If the software in block 312 determines that there are elements thatstill require new capital value driver models, then the processingdescribed in the preceding paragraphs is repeated. Alternatively, if thesoftware in block 312 determines that there are “current” capital valuedrivers for all elements in all enterprises, then processing advances toa software block 317. The software in block 317 checks the element ofvalue definition table (153) and sub-element definition table (154) todetermine if the user (20) has specified that there will be sub-elementsof value for any of the elements. If the user (20) has specified thatthere will be no sub-elements of value, then processing advances to asoftware block 375 where the elements are checked for interaction.Alternatively, if there are elements of value with sub-elements, thenprocessing advances to a software block 318. The software in block 318checks the element of value definition table (153) to determine thenumber of elements that have sub-elements before advancing processing toa block 319.

The software in block 319 retrieves the element of value definition forthe next element with defined sub-elements from the element of valuedefinition table (153) before advancing processing to a block 321. Thesoftware in block 321 checks the sub-element definition table (154) todetermine if the sub-elements assignments for all items within theelement are “current”. If the sub-element assignments are “current”,then processing returns to block 318 which checks to see if all elementswith sub-elements have been reviewed in the current cycle of processing.If the software in block 318 determines that all elements have beenreviewed, then system processing advances to a software block 332.Alternatively, if there are elements still need to be reviewed, thenprocessing returns to block 319 as described previously. If the softwarein block 321 determines that the sub-element assignments are not“current”, then processing advances to a block 326 where the sub-elementassignments are completed.

The software in block 326 checks the system settings table (140) todetermine if the calculation being completed is a stand-alonecalculation or a comparison to a prior calculation. If the software inblock 326 determines that the current calculation is not being used fora comparison, then the processing advances to a software block 322. Thesoftware in block 322 retrieves the value driver data by item for theelement being analyzed from the composite variable data table (168)before creating a normalized set of value driver data for each itemwithin the element of value being analyzed. The normalized value foreach value driver data element for each item in each period is thencalculated using Formula 3 shown below.

$\begin{matrix}{{{Normalized}\mspace{14mu} {Value}} = \frac{{{Current}\mspace{14mu} {value}} - {Mn}}{\left( {{MP} - {MN}} \right)}} & {{Formula}\mspace{14mu} 3}\end{matrix}$

-   Where: MN=minimum positive or most negative data value for all    element items MP=maximum positive data value for all element items

After the normalized data are saved in the normalized composite variabledata table (169) in the application database (50), system processingadvances to a software block 323. The software in block 323 uses anunsupervised “Kohonen” neural network that uses competitive learning tocreate a clustering scheme and segment the element of value. As shown inFIG. 11 a “Kohonen” network has only two layers—an input layer (712) andan output layer (713). The input layer (712) holds the input nodes(750-x) where the different inputs are sequentially entered. The inputpatterns are transmitted to an output layer (713) which has one node(760-x) for each possible output category. The input layer and theoutput layer are fully interconnected as shown in FIG. 11. The differentvariables are defined in Table 24.

TABLE 24 Variable Definition P The number of items for the element.Equals the number of different patterns that will be presented to thenetwork M The number of variables the in the composite variable for theelement as well as the number of input nodes (750-1 through 750-M) N Themaximum number of sub-elements for this element (default is 20) as wellas the number of output nodes (760-1 through 760-N) ω_(ij) Representsthe connection strength between unit j of the input layer (712) and uniti of the output layer (713) X_(j) Represents the input vector which isthe normalized value of the “j^(th)” item composite variables V_(i)Matching value - measures how closely the weights of a given nodematches the input vector

“Kohonen” network processing begins when the software in block 323initializes at random the weights (716) between the output layer (713)and the input layer (712) with small values. In the next step the systemstarts sequentially entering the normalized composite variable data fromthe normalized composite variable data table (169) into the input layer(712). The normalized value for each variable is entered into adifferent input node (750-x) and transmitted from there to the outputlayer (713). The nodes in the output layer (760-x) each compute theirmatching values (V_(i)) using Formula 4 shown below.

V _(I)=Σ(ω_(ij) −x _(j))²  Formula 4

The matching value (V_(i)) essentially represents the distance betweenthe vectors (ω_(i)) and x. Therefore, the output node (760) with thelowest matching value is also the node that most closely matches theinput vector. The unit that is closest to the input is declared thewinner and its weight (ω_(ij)) along with the weights of the neighboringoutput nodes are updated. The change in weight for the winning node andits neighbors is calculated using Formula 5 shown below.

Δω_(ij)=α(x _(j)−ω_(ij)) where: α represents the learning rate (seeFormula 6)  Formula 5

The application of this formula diminishes the difference between theweights of the output nodes and the weights of the input vectors. Outputnodes that are not neighbors of the winning node are not updated. Theoutput nodes are updated after each input and over time the applicationof the formulas shown above will tend to create clusters of similarnodes.

The input vectors (data patterns) are cycled through the “Kohonen”network a pre-determined number of times which are referred to asepochs. The total number of epochs (T) will be set by the software tosomewhere between 500 and 10,000 depending upon the number of compositesort variables used for the element. The neighborhood size, that is thequantity of adjacent nodes that are considered to be neighbors, isadjusted downward from its initial value of 75% of the value of N by onenode at a time as the number of epochs increases from zero (0) to itsmaximum number (T). The learning rate (α) is determined by Formula 6shown below.

α=0.2×(1−(T/10,000))  Formula 6

Once the Kohonen network processing has been completed for the specifiednumber of epochs (T), the software in block 323 arbitrarily assigns anumber to each output node (760-x). The software in block 323 thencalculates the distance between the input vector (x) of each item andthe weight in each output node (760-x) using Formula 4. The software inblock 323 then assigns the number of the closest output node (760-x) tothe item and stores the resulting information in the sub-elementdefinition table (154) in the application database (50). The software inblock 323 also stores the final value of all network weights in thesub-element weights table (157) in application database (50).

After the network weights and information assigning each item to asub-element have been stored in the appropriate tables in theapplication database (50), processing returns to software block 318 andthe process described above is repeated until all elements withsub-elements of value have been reviewed.

If the software in block 326 determines that the calculation beingcompleted is a comparison to a prior valuation, then processing advancesto a software block 327. The software in block 327 retrieves thesub-element weights from the previous calculation from the sub-elementweights table (157) and reestablishes the prior sub-element assignmentsby using Formula 4 to determine the appropriate sub-element assignmentfor each item. When this processing has been completed, processingadvances to a software block 328.

The software in block 328 checks the composite variable data table (168)to see if there are any new items for elements being analyzed. If thereare no new items, then processing returns to block 318 as describedpreviously. Alternatively, if the software in block 328 determines thatthere are new items, then processing advances to a software block 329.

The software in block 329 determines the appropriate sub-elementassignment for each new item by calculating the normalized value of theinput vector for each new item and using formula 4 to determine whichoutput node (i.e., which sub-element from the previous calculation) eachitem should be assigned to. The inputs for these calculations are storedin the normalized composite variable data table (169) and the resultsare stored in the composite variable data table (168) in the applicationdatabase (50) before processing returns to block 318 as describedpreviously.

When the software in block 318 determines that all elements have beenreviewed, processing advances to block 332 as described previously. Thesoftware in block 332 checks the composite variable (156), revenuedriver (179), expense driver (180) and capital driver (181) tables todetermine if the value drivers and composite variables for allsub-elements are current. If they are current, then processing advancesto a software block 375. Alternatively, if the sub-element drivers andcomposite variables are not current, then processing advances to a block337.

The software in block 337 checks the revenue driver table (179) in theapplication database (50) to determine if all enterprise revenuesub-components have “current” drivers and composite variables for allelements. If all enterprise revenue sub-components have “current”drivers and composite variables for all elements, then processingadvances to a software block 341. Alternatively, if there are anyrevenue components without “current” drivers for at least one element,then processing advances to a software block 338. The software in block338 uses the sub-element definition table (154) and excluded variablestable (182) to guide the retrieval of information required to specifythe next revenue driver model that is being updated. All informationrelated to the enterprise element being examined less any informationidentified in the excluded variables table (182) is retrieved by block338 from the revenue driver table (179) before processing advances to asoftware block 339.

The software in block 339 calculates performance indicators by item foreach date and numeric data field in a manner similar to that describedfor block 304. After the item performance indicators are calculated andstored in the revenue driver table (179) in the application database(50), processing advances to a software block 305. The software in block305 creates a predictive time series neural net model for the revenuedriver as described previously. After the revenue value driverpredictive model has been specified, processing proceeds through blocks325, 330, 335, 340, 345, 350 in a manner identical to that describedpreviously for the processing of the revenue value driver model beforeadvancing to a block 355.

The software in block 355 uses a model selection algorithm to identifythe induction algorithm that best fits the data for the element beingexamined. For the system of the present invention, a cross validationalgorithm is used for model selection. The software in block 360optimizes each of the induction algorithms using nine (9) of the ten(10) sets of data. As part of this processing, the duplication of theinformation related to each item is eliminated as only the strongestcausal factor variables are included in the final solution. Thecomposite variable from each induction algorithm is then tested usingthe data from the remaining set to identify the causal model thatproduces the best fit for that set of test data. The previouslycalculated composite variable for the revenue element is also comparedto the sub-element composite variables as part of this processing. Thisprocess is repeated ten (10) times which allows each subgroup to be usedas the basis for validating model performance. The composite variableand value drivers that produced the best results are then saved in thecomposite variable table (156), composite variable data table (168) andrevenue driver table (179) in the application database (50) andprocessing returns to a block 337.

If the software in block 337 determines that there are sub-elements thatstill require new revenue value driver models, then the processingdescribed in the preceding paragraphs is repeated. Alternatively, if thesoftware in block 337 determines that there are “current” revenue valuedrivers for all sub-elements in all enterprises, then processingadvances to a software block 341. The software in block 341 checks theexpense driver table (180) in the application database (50) to determineif all enterprise expense components have “current” drivers for allelements. If all enterprise expense components have “current” driversfor all sub-elements, then processing advances to a software block 352.Alternatively, if there are expense sub-components without “current”drivers for at least one element, then processing advances to a softwareblock 342. The software in block 342 uses the sub-element definitiontable (154) and excluded variables table (182) to guide the retrieval ofinformation required to specify the next expense driver model that isbeing updated. All information related to the enterprise sub-elementbeing examined less any information identified in the excluded variablestable (182) is retrieved by block 342 from the expense driver table(180) before processing advances to a software block 343.

The software in block 343 calculates expressions by item for eachnumeric data field and each date field in manner identical to thatdescribed previously for software block 339. After the item performanceindicators are calculated and stored in the expense driver table (180)in the application database (50), processing advances to a softwareblock 309. The software in block 309 creates a predictive time seriesneural net model for the expense value driver as described previously.After the expense value driver predictive model has been specified,processing proceeds through blocks 325, 330, 335, 340, 345, 350 and 355in a manner identical to that described above for the processing of therevenue value driver model before returning to block 341.

If the software in block 341 determines that there are sub-elements thatstill require new expense value driver models, then the processingdescribed in the preceding paragraphs is repeated. Alternatively, if thesoftware in block 341 determines that there are “current” expense valuedrivers for all sub-elements in all enterprises, then processingadvances to a software block 352. The software in block 352 checks thecapital driver table (181) in the application database (50) to determineif all enterprise capital components have “current” drivers for allelements. If all enterprise capital components have “current” driversfor all sub-elements, then processing advances to software block 375.Alternatively, if there are capital sub-components without “current”drivers for at least one element, then processing advances to a softwareblock 353. The software in block 353 uses the sub-element definitiontable (154) and excluded variables table (182) to guide the retrieval ofinformation required to specify the next capital driver model that isbeing updated. All information related to the enterprise sub-elementbeing examined less any information identified in the excluded variablestable (182) is retrieved by block 353 from the capital driver table(181) before processing advances to a software block 354.

The software in block 354 calculates expressions by item for eachnumeric data field and each date field in manner identical to thatdescribed previously for software blocks 339 and 343. After the itemperformance indicators are calculated and stored in the capital drivertable (181) in the application database (50), processing advances to asoftware block 315. The software in block 315 creates a predictive timeseries neural net model for the capital driver as described previously.After the capital value driver predictive model has been specified,processing proceeds through blocks 325, 330, 335, 340, 345, 350 and 355in a manner identical to that described above for the processing of therevenue value driver model before returning to block 352.

If the software in block 352 determines that there are sub-elements thatstill require new capital value driver models, then the processingdescribed in the preceding paragraphs is repeated. Alternatively, if thesoftware in block 352 determines that there are “current” capital valuedrivers for all sub-elements in all enterprises, then processingadvances to software block 375.

The software in block 375 checks the value drivers to determine if thereis any interaction between drivers for different elements. Ifinteraction between element drivers is discovered, then processingadvances to a block 402 where the valuation of growth options isstarted. Alternatively, if no interaction between element drivers isfound, then system processing is completed in accordance with thespecification of U.S. patent application Ser. No. 08/779,109.

Growth Option Valuation

The flow diagram in FIG. 7 details the processing that is completed bythe portion of the application software (400) that calculates the valueof the growth options for each enterprise. Processing begins in block402 where the software in the block checks the growth option value table(178) to determine if there are “current” valuations for all definedgrowth options. If there are no defined growth options or no growthoptions that require a new valuation, then processing advances to atransfer block 403 and on to a block 501 where the components of valueare analyzed.

If there are growth options that need a value calculation completed,then system processing advances to a software block 404 where thesoftware in the block retrieves the previously stored data from thegrowth option definition table (175) for the next growth option and thenadvances processing to a block 405. The software in block 405 checks thegrowth option definition table (175) to determine if there are multiplescenarios for the growth option being analyzed. If there is only onescenario for the growth option being analyzed, then processing advancesto a block 407. Alternatively, if there are multiple scenarios, thenprocessing advances to a block 408.

The software in block 408 prompts the user (20) via the growth optionscenario definition data window (916) to input or update, the number ofscenarios, the name of the scenarios and the probability that eachscenario will occur for the growth option being valued. The probabilityof each scenario is specified as a percentage. The sum of the scenarioprobabilities must equal 100% for each growth option. The user (20) isallowed to change the scenarios even if the system (100) is calculatinga business valuation comparison as the comparison will be made at thegrowth option level not at the scenario level. The input from the user(20) is stored in the growth option definition table (175) in theapplication database (50) before processing advances to a block 409.

The software in block 409 checks the growth option scenario table (177)in the application database (50) to determine if there are “current”data for every scenario listed in the growth option definition table(175). If there are “current” data for all scenarios, then processingadvances to block 407 where the value of the total deductions fromrevenue components, expense sub-components, capital sub-components andelements of value for the growth option are calculated. Alternatively,if some or all of the scenarios for the growth option being examineddon't have “current” data, then processing advances to a block 410. Thesoftware in block 410 retrieves the information for the scenario fromthe growth option scenario table (177) and advances processing to asoftware block 411.

The software in block 411 generates a form that is displayed using ascenario revenue and expense data window (917) for the user (20) tocomplete. The form identifies the time periods that revenue and expenseforecasts are required for the growth option scenario in accordance withthe calculations previously completed by the application software andstored in the system settings table (140). The forecast information theuser (20) provides is saved to the growth option scenario table (177) inthe application database (50). If the scenario being examined is thefirst scenario for the growth option, then the user (20) is also askedto quantify any growth option related expenses by account number thatwere incurred in prior periods. The user (20) is not asked to identifyany prior period growth option revenue as a growth option is bydefinition a project that will lead to revenue at some date in thefuture. The information regarding prior period expenses is saved inexpense data table (142) in the application database (50). The user (20)is also asked to identify the months where the prior expenses and/or theforecast revenue and the forecast expenses for the growth optionscenario were included in the overall company totals. The users inputregarding the overlapping periods for the scenario is saved in thegrowth option overlap table (176) in the application database (50) andprocessing advances to a software block 412.

The software in block 412 prompts the user (20) via a scenario capitaldata window (918) to edit or provide a forecast of the capitalrequirements for the scenario by month for the time periods required forgrowth option valuation in accordance with the calculations previouslycompleted by the application software and stored in the system settingstable (140). The forecast information the user (20) provides is saved tothe growth option scenario table (177) in the application database (50).If the scenario being examined is the first scenario for the growthoption, then the user (20) is also asked to quantify any growth optionrelated capital investments by account number that were present in priorperiods. The information regarding prior period capital requirements issaved in the capital data table (143) in the application database (50).The user (20) is also asked to identify the months where the priorperiod actual and/or forecast capital requirements for the growth optionscenario were included in the overall company totals. The users inputregarding overlapping periods for the scenario is saved in the growthoption overlap table (176) in the application database and processingadvances to a software block 413.

The software in block 413 prompts the user (20) via a scenario elementof value data window (919) to edit or provide a forecast of element ofvalue usage by month for the time periods required for growth optionvaluation in accordance with the calculations previously completed bythe application software. The forecast information the user (20)provides is saved to the growth option scenario table (177) in theapplication database (50). If the scenario being examined is the firstscenario for the growth option, then the user (20) is also asked toidentify any growth option related element of value usage that occurredin prior periods. The information regarding prior period use of theelements of value is saved in the element of value data table (144). Theuser (20) is also asked to identify the months where the prior periodand/or forecast element of value usage for the growth option wereincluded in the overall company totals. The users input regardingoverlapping periods is saved in the growth option overlap table (176) inthe application database and processing returns to a software block 409.

If the software in block 409 determines that there are still scenariosthat don't have “current” data, then the processing sequence describedabove is repeated until all scenarios have “current” data. When allscenarios for the growth option being analyzed have “current” data,processing advances to block 407. The software in block 407 calculatesthe total value of revenue, expense, capital and element of valuedeductions for each scenario. The software in block 407 also calculatesthe weighted average forecast of total growth option revenues,expenditures, capital and element of value deductions for each period bymultiplying the forecast revenue, capital and element of valuedeductions for each scenario by the probability of that scenariorealization. The totals for the growth option revenue, expense, capital,and element of value deductions are then saved in the appropriate datatables (141 through 146) in the application database (50). After thedata have been stored, processing advances to a software block 406 wherethe value of the growth option is calculated using dynamic programmingalgorithms in a manner that is well known. The process described in thepreceding paragraphs is repeated until all growth options have “current”valuations and processing advances to block 502 as described previously.

Analyze Components of Value

The flow diagram in FIG. 8 details the processing that is completed bythe portion of the application software (500) that analyzes thecomponents and sub-components of value. Processing begins in a softwareblock 502. The software in block 502 checks the enterprise value table(170) in the application database (50) to determine if there are“current” valuations for all enterprises for the date for which thecurrent valuation is being calculated. If there are “current” valuationsfor all enterprises, then processing advances to a software block 515where the software in the block calculates the total company currentoperation value. However, if some or all of the enterprises don't have“current” valuations, then processing advances to a software block 503.

The software in block 503 retrieves the definition for the nextenterprise that doesn't have a “current” valuation from the enterprisedefinition table (155) in the application database (50). Processing thenadvances to a software block 504. The software in block 504 checks thedata from the revenue component definition table (150) for the revenuecomponent of the enterprise being valued to determine if there is a“current” valuation for the component. If there is a “current” valuationfor the revenue component, Then processing advances to a software block507 where the values of the expense component or expense sub-componentsfor the enterprise are checked to determine if they are “current”.However, if the revenue component valuation isn't “current”, thenprocessing advances to a software block 505. The software in block 505retrieves the information for the revenue component from the revenuedata table (141) and processing advances to a software block 506. Inaccordance with the present invention, the revenue component value iscalculated for the specified date of valuation using Formula 7 shownbelow.

$\begin{matrix}{{{Value} = {{F_{f\; 1}/\left( {1 + K} \right)} + {F_{f\; 2}/\left( {1 + K} \right)^{2}} + {F_{f\; 3}/\left( {1 + K} \right)^{3}} + {F_{f\; 4}/\left( {1 + K} \right)^{4}} + {\left( {F_{f\; 4} \times \left( {1 + g} \right)} \right)/\left( {\left( {K - g} \right) \times \left( {1 + K} \right)^{4}} \right)}}}\begin{matrix}\text{Where:} & \; \\F_{fx} & {{= {{Forecast}\mspace{14mu} {revenue}}},{{expense}\mspace{14mu} {or}\mspace{14mu} {capital}\mspace{14mu} {for}\mspace{14mu} {year} \times}} \\\; & {{valuation}\mspace{14mu} {date}\mspace{11mu} \left( {{from}\mspace{14mu} {advanced}\mspace{14mu} {financial}\mspace{14mu} {system}} \right)} \\K & {= {{{Cost}\mspace{14mu} {of}\mspace{14mu} {capital}} - {\% \mspace{14mu} {per}\mspace{14mu} {year}\mspace{11mu} \left( {{from}\mspace{14mu} {system}\mspace{14mu} {settings}} \right)}}} \\g & {= {{{Forecast}\mspace{14mu} {growth}\mspace{14mu} {rate}\mspace{14mu} {to}\mspace{14mu} {perpetuity}} -}} \\\; & {\% \mspace{14mu} {per}\mspace{14mu} {year}\mspace{11mu} \left( {{from}\mspace{14mu} {advanced}\mspace{14mu} {financial}\mspace{14mu} {system}} \right)}\end{matrix}} & {{Formula}\mspace{14mu} 7}\end{matrix}$

After the valuation of the revenue component is complete, the result isstored in the revenue component definition table (150) in theapplication database (50) and processing advances to a software block507.

The software in block 507 checks the expense component definition table(151) in the application database (50) to determine if there are“current” valuations for all expense components or sub-components in theenterprise being valued. If the user (20) has previously storedinformation in the system settings table (140) specifying a “simplified”analysis, then the expense component values will be checked.Alternatively, if the user (20) has not selected a simplified analysis,then the expense sub-component values will be checked. If there are“current” valuations for the expense components or all sub-components,then processing advances to a block 510 where the values of the capitalcomponents for the company are checked to determine if they are“current”. However, if some or all of the expense components orsub-components don't have “current” valuations, then processing advancesto a software block 508. The software in block 508 retrieves theinformation from the expense data table (142) for the expense componentor the next expense sub-component that doesn't have a “current”valuation. Processing then advances to a software block 509. Inaccordance with the present invention the valuation of the expenses iscalculated for the specified date of valuation using Formula 7. Afterthe valuation of the expense component or expense sub-component has beencompleted, the results are stored in the expense component definitiontable (151) in application database (50) and processing returns to asoftware block 507. If there are still expense sub-components that don'thave current valuations, then the processing described above is repeatedfor the next sub-component. Alternatively, if the expense component orall expense sub-components have current valuations, then processingadvances to a software block 510.

The software in block 510 checks the capital component definition table(152) in the application database (50) to determine if there are“current” valuations for all capital components. If the user (20) haspreviously stored information in the system settings table (140)specifying a “simplified” analysis, then the capital component value forthe enterprise will be checked. Alternatively, if the user (20) has notselected a simplified analysis, then the standard capital sub-componentswill be checked. If there are “current” valuations for all capitalcomponents, then processing advances to a software block 514 where theenterprise current operation value is calculated. If the valuation forthe capital component or some of the capital sub-components is not“current”, then processing advances to a software block 511. Thesoftware in block 511 retrieves the information required for valuationof the next capital component or sub-component that doesn't have a“current” valuation from the capital data table (143) in the applicationdatabase (50). Processing then advances to a software block 512. Thesoftware in block 512 calculates the value of the capital component orcapital sub-component using Formula 7. After the valuation of thecapital component or a capital sub-component is complete, the resultsare stored in the capital component definition table (152) in theapplication database (50) and system processing returns to block 510. Ifthere are still capital sub-components that don't have currentvaluations, then the processing described above is repeated for the nextsub-component. Alternatively, if the capital component or all capitalsub-components have current valuations, then processing advances to asoftware block 514.

The software in block 514 calculates the current operation value of eachenterprise by summing the previously calculated component andsub-component values as shown in Table 4. The calculated value for theenterprise current operation is stored in the enterprise value table(170) in the application database (50) and processing returns to block502 which again checks the enterprise value table (170) in theapplication database (50) to determine if all enterprises have “current”values. If there are still enterprises without “current” values, thenprocessing advances to block 503 and the processing described in thepreceding paragraphs is repeated for another enterprise. Alternatively,if all the enterprises have “current” values, then processing transfersto a block 515 where the software in the block adds the enterprisevalues together to calculate the value of the current-operation for thetotal company. The total company current-operation value is stored inthe enterprise value table (170) in the application database (50) andprocessing advances to a software block 602 where the predictive modelspecification and optimization is started.

Predictive Model Specification and Optimization

The flow diagrams in FIG. 9A, FIG. 9B and FIG. 9C detail the processingthat is completed by the portion of the application software (600) thatuses predictive models to determine the relationship between the valuedrivers and the revenue, expense and capital components of all definedenterprises. Processing begins in software block 602. The software inblock 602 checks the revenue model weights table (159) in theapplication database (50) to determine if the revenue components for allenterprises have “current” models. If there are revenue componentswithout “current” predictive models, then processing advances to asoftware block 603 where the information specifying the next revenuecomponent is retrieved from the revenue component definition table (150)in the application database (50). After the revenue component definitionhas been retrieved, processing advances to a software block 604 wherethe software in the block creates a predictive time series neural netmodel for the revenue component. More specifically, the software in theblock creates a neural network model that relates the value drivers fora given enterprise to the revenue component.

The software in block 604 determines the number of the input nodes andhidden nodes for the network as a function of the number of valuedrivers associated with the enterprise revenue component. There are alsoadditional input nodes for prior period revenue and for a thresholdnode. The software in block 604 sets the initial number of hidden layersto one. The software in block 604 also establishes one output node forthe revenue and sets all weights to random numbers between 0 and 1(except the threshold node weight which is fixed at 1). The processingcompleted by the network nodes (710-x, 720-x and 730) was describedpreviously. After the network architecture including the nodes has beenspecified by the software in block 604, processing advances throughblocks 325, 330, 335 and 340 as described previously.

The process of evolving a preliminary solution continues until the newparallel populations fail to reach a new target level and processing isthen advanced to a block 625. As part of this processing revenue model,genes are stored in the revenue model gene table (158) in a manneridentical to that described previously for the storage of model genes.The software in block 625 uses the chromosome that achieved the highestfitness to initialize a feed-forward neural network. In a manner that iswell known, the network is then trained by the software in a block 630using a traditional backpropagation algorithm to further minimize theerror function associated with the network. The resulting weights forthe enterprise are then saved in the revenue model weights table (159)in the application database (50) and processing returns to a block 602.

If the software in block 602 determines that there are “current” revenuemodels for all enterprises, then processing advances to a software block605. The software in block 605 checks the expense model weights table(161) in the application database (50) to determine if the expensecomponent or all expense sub-components have “current” models. If theuser (20) has previously stored information in the system settings table(140) specifying a “simplified” analysis, then the expense componentmodel will be checked before processing advances to another block.Alternatively, if the user (20) has not selected a simplified analysis,then the expense sub-component models will be checked before processingadvances to another block. In either case, processing will advance toblock 607 if the expense models aren't “current” and to block 611 ifthey are “current”.

The software in block 607 retrieves the information specifying theexpense component or the next expense sub-component from the expensecomponent definition table (151) in the application database (50). Afterthe required information is retrieved, processing advances to a block608 where the predictive expense model is specified in a manner similarto that described previously for the predictive revenue model. Fromblock 608, processing advances to blocks 325, 330, 335, 340, 625 and 630where the genetic evolution of the fittest solution is completed in amanner similar to that described above for the predictive revenue model.As part of this processing, expense model genes are stored in theexpense model gene table (160) in a manner identical to that describedpreviously for the storage of revenue model genes. If there aresub-components, then the process described above is repeated until allexpense sub-components have “current” models. When all expensecomponents or all expense sub-components have “current” models,processing advances to a software block 611.

The software in block 611 checks the capital model weights table (163)in the application database (50) to determine if the capital componentor all capital sub-components have “current” models. If the user (20)has previously stored information in the system settings table (140)specifying a “simplified” analysis, then the capital component modelwill be checked before processing advances to another software block.Alternatively, if the user (20) has not selected a simplified analysis,then the standard capital sub-component models will be checked beforeprocessing advances to another software block 613. In either case,processing will advance to block 613 if the models aren't “current” andto block 772 if they are “current”.

The software in block 613 retrieves the information specifying thecapital component or the next capital sub-component from the capitalcomponent definition table (152) in the application database (50). Afterthe required information is retrieved, processing advances to a block614 where the predictive capital model is specified in a manner similarto that described previously for the predictive revenue and expensemodels. From block 614, processing advances to blocks 325, 330, 335,340, 625 and 630 where the genetic evolution of the fittest solution iscompleted in a manner similar to that described above for the predictiverevenue and expense model. As part of this processing, capital modelgenes table (162) in a manner identical to that described previously forthe storage of revenue and expense model genes. If there aresub-components, then the process described above is repeated until allcapital sub-components have “current” models. When all capitalcomponents or all capital sub-components have “current” models,processing advances to a block 772 where valuations are calculated forthe elements and sub-elements of value.

Value All Elements and Sub-Elements of Value

The flow diagram in FIG. 12 details the processing that is completed bythe portion of the application software (700) that values all elementsand sub-elements of current-operation value for all enterprises.Processing begins in software block 772. The software in block 772checks the revenue component percentage table (164) in the applicationdatabase (50) to determine if the revenue component models for allenterprises have “current” percentages. If there are revenue componentswithout “current” percentages, then processing advances to a block 773where the information specifying the next revenue component is retrievedfrom the revenue component definition table (150) and the revenue modelweights table (159) in the application database (50).

After the revenue component information is retrieved, processingadvances to a block 774 where relationships between the elements andsub-elements of value and the revenue component are determined. Thesoftware in block 774 uses the network weights (706 and 707) previouslystored in the revenue model weights table (159) to segregate thehidden-layer (703) to output-layer (704) connection weights (707) foreach hidden node (720-x) into the components associated with each inputnode (710-x). The portion of the output attributable to each input nodeis then determined by Formula 8 (shown below).

$\begin{matrix}{\left( {\sum\limits_{k = 1}^{k = m}{\sum\limits_{j = 1}^{j = n}{l_{jk} \times {O_{k}/{\sum\limits_{j = 1}^{j = n}l_{ik}}}}}} \right)/{\sum\limits_{k = 1}^{k = m}{\sum\limits_{j = 1}^{j = n}{l_{jk} \times O_{k}}}}} & {{Formula}\mspace{14mu} 8}\end{matrix}$

Where

-   -   I_(jk)=Absolute value of the input weight (706) from input node        j to hidden node k    -   O_(k)=Absolute value of output weight (707) from hidden node k    -   m=number of hidden nodes    -   n=number of input nodes        After Formula 8 is solved by the software in block 774, the        portion of the revenue value attributable to each element or        sub-element of value is calculated by adding together the        percentages from all value drivers associated with each element        or sub-element of value. The result of these calculations is        then stored in the revenue component percentage table (164) in        the application database (50). The portion of the revenue value        that can't be attributed to an element or sub-element of value        is generally the portion that is attributed to the prior period        revenue. This portion of the value will be referred to as going        concern revenue component. After the storage of the revenue        component percentages has been completed, processing returns to        block 772. The software in block 772 checks the application        database (50) to determine if all revenue components have        “current” model percentages. If there are still revenue        components without “current” percentages, then the system        repeats the processing described in the preceding paragraphs.        Alternatively, if all of the revenue component models have        “current” percentages, then processing advances to a software        block 775.

The software in block 775 checks the expense component percentage table(165) in the application database (50) to determine if all expensecomponent or sub-component models for all enterprises have “current”percentages. If the user (20) has previously stored information in thesystem settings table (140) specifying a “simplified” analysis, then theexpense component percentages will be checked. Alternatively, if theuser (20) has not selected a simplified analysis, then the expensesub-component percentages will be checked. If there are expensecomponents or sub-components without “current” percentages, thenprocessing advances to a software block 776 where the informationspecifying the next expense component or sub-component is retrieved fromthe expense component definition table (151) and the expense modelweights table (161) in the application database (50). After the expensecomponent or sub-component information is retrieved, processing advancesto a software block 777 where the percentages of value for the expensecomponent or sub-component are calculated in a manner identical to thatdescribed previously for revenue components. The portion of the expensevalue that can't be attributed to an element or sub-element of value isgenerally the portion that is attributed to the prior period expense.This portion of the value will be referred to as going concern expensecomponent. After the storage of the percentages of the expense componentor sub-component to the expense component percentage table (165) hasbeen completed, processing returns to block 775. The software in block775 checks the expense component percentage table (165) in theapplication database (50) to determine if all expense component orsub-component models have “current” percentages. If there are stillexpense component or sub-component models without “current” percentages,then the system repeats the processing described above. Alternatively,if all of the expense component or sub-component models have “current”percentages, then processing advances to a software block 778.

The software in block 778 checks the capital component percentage table(166) in the application database (50) to determine if all capitalcomponent or sub-component models for all enterprises have “current”percentages. If the user (20) has previously stored information in thesystem settings table (140) specifying a “simplified” analysis, then thecapital component percentages will be checked. Alternatively, if theuser (20) has not selected a simplified analysis, then the capitalsub-component percentages will be checked. If there are capitalcomponent or sub-component models without “current” percentages, thenprocessing advances to a software block 779 where the informationspecifying the next capital component or sub-component is retrieved fromthe capital component definition table (152) and the capital modelweights table (163) in the application database (50). After the capitalcomponent or sub-component information is retrieved, processing advancesto a software block 780 where the percentages of value for the capitalcomponent or sub-component are calculated in a manner identical to thatdescribed previously for revenue and expense components. The portion ofthe capital element or sub-element value that can't be attributed to anelement or sub-element of value is generally the portion that isattributed to the prior period capital requirements. This portion of thevalue will be referred to as going concern capital value. After thestorage of the percentages of the capital component or sub-component tothe capital component percentage table (166) has been completed,processing returns to block 778. The software in block 778 checks thecapital component percentage table (166) in the application database(50) to determine if all capital components or sub-components have“current” percentages. If there are still capital component orsub-component models without “current” percentages, then the systemrepeats the processing described above (779 and 780). Alternatively, ifall of the capital components or sub-components have “current”percentages, then processing advances to a software block 781.

The software in block 781 combines all the revenue component, expensecomponent or sub-component and capital component or sub-component valuestogether to calculate the overall value for each element or sub-elementof value by enterprise as shown in Table 4. As part of the processing inthis block, the calculated value of production equipment element (orsub-elements) of value is compared to the liquidation value for theequipment in the element. The stored value for the element (orsub-elements) will be the higher of liquidation value or calculatedvalue. After the calculations are completed, processing advances to asoftware block 782 where the residual going concern value is calculatedusing Formula 9.

Residual Going Concern Value=Total Current-Operation Value−ΣFinancialAsset Values−ΣElements of Value−ΣSub-Elements of Value  Formula 9

After the residual going concern value is calculated for eachenterprise, the values calculated for each element and sub-element ofvalue (including going concern value) by the software in blocks 781 and782 are stored by enterprise in the enterprise value table (170) in theapplication database (50). System processing then advances to a softwareblock 802 where the preparation of the management reports is started.

Display and Print Results

The flow diagram in FIG. 13 details the processing that is completed bythe portion of the application software (800) that creates, displays andoptionally prints financial management reports. The primary managementreport, the Value Map™ report, summarizes information about the elementsand sub-elements of business value on the valuation date. If acomparison calculation has been completed, a Value Creation report canbe generated to highlight changes in the elements and sub-elements ofbusiness value during the period between the prior valuation and thecurrent valuation date.

System processing in this portion of the application software (800)begins in block 802. At this point in system processing, virtually allof the information required to produce the Value Map™ report has beencalculated using the methods outlined in Table 1 as detailed in thepreceding sections. As a result, the only computation that needs to bemade is the calculation of economic equity. The software in block 802retrieves the required information from the enterprise value table(170), debt data table (174) and equity data table (144) in theapplication database (50) and then calculates the economic equity forthe business as a whole using Formula 10 (shown below).

Economic Equity=(Current Operation Value)+(ΣGrowth OptionValues)−(Current Liabilities)−(Current Debt)−(Book*EquityValue)  Formula 10

*calculated in accordance with GAAPAn equity value for each enterprise is then calculated by dividing thecombined book and economic equity as required to balance the Value Map™report totals in accordance with Formula 11 (shown below).

Enterprise Equity=(Enterprise Current Operation Value)+(ΣEnterpriseGrowth Option Values)−(Current Enterprise Liabilities)−(CurrentEnterprise Debt)  Formula 11

where Σ(Enterprise Equity)=Book*Equity+Economic Equity

*calculated in accordance with GAAPAfter the economic equity value and the enterprise equity values arecalculated and stored in the economic equity values table (171), asummary Value Map™ report (see FIG. 14 for format) for the entirecompany is created and stored in the reports table (172) and processingadvances to a software block 803. The software in block 803 checks thesystem settings table (140) to determine if the current valuation isbeing compared to a previous valuation. If the current valuation is notbeing compared to a previous valuation, then processing advances to asoftware block 805. Alternatively, if the current valuation is beingcompared to a previously calculated valuation, then processing advancesto a software block 804.

The software in block 804 calculates Value Creation Statements (see FIG.15 for format) for each enterprise and for the business as a whole forthe specified time period After the Value Creation Statements are storedin the reports table (172) in the application database (50), processingadvances to a software block 805. The software in block 805 displays thesummary Value Map™ report to the user (20) via a report data window(909).

After displaying the summary Value Map™ report, system processingadvances to a software block 806 where the user is prompted via a reportselection data window (915) to designate additional reports forcreation, display and/or printing. The report selection data window(915) also gives the user (20) the option of having a report created toanalyze the relationship between the market value of the business andthe calculated business value. The user (20) has the option of creating,displaying or printing the Value Map™ reports for the company as a wholeand/or for any combination of the enterprises within the company. Theuser (20) can also choose to create, display or print a Value CreationStatement for the business as a whole and/or for any combination ofenterprises if a comparison calculation were being made. The software inblock 806 creates and displays all Value MapS™ reports and ValueCreation Statements requested by the user (20) via the report selectiondata window (915). After the user (20) has completed the review ofdisplayed reports and the input regarding equity analysis and reports toprint has been stored in the reports table (172), processing advances toa software block 807. The software in block 807 transfers processing toa software block 808 if the user (20) has chosen to have therelationship between market value and calculated business valueexamined. The software in block 808 compares the market value of thebusiness to the calculated value by completing the Formula 12 for eachcomplete valuation stored in the reports table (172).

((ΣE×N)−D)=(Y×V)  Formula 12

Where:

E=Market price of equity for valuation date

N=Number of shares of equity outstanding on valuation date

D=Market value of debt on valuation date

Y=Market value/calculated business value ratio

V=Total calculated business value on the valuation date

The average ratio of market value to calculated business value and thestandard deviation of the ratio are then calculated using standardregression analysis methods and stored in the equity forecast table(148) in the application database.

If the date of the current valuation is more than 60 days after thecurrent system date, then the software in block 808 will calculate arange for equity prices on the valuation date by combining the resultsof previous calculations of the relationship between equity value andcalculated value with the forecast of future value that was justcompleted. The software will calculate the future equity value rangeusing both the average ratio of total business value to total marketvalue. The software in block 808 then prepares a report summarizing theresults of the preceding calculations that is stored in the reportstable (172) in the application database (50) and processing advances toa software block 809. If the user (20) elects not to complete thecalculated valuation versus equity price analysis, then the software inblock 807 advances processing directly to a software block 809.

The software in block 809 checks the reports tables (172) to determineif any reports have been designated for printing. If reports have beendesignated for printing, then processing advances to a block 810 whichsends the designated reports to the printer (118). After the reportshave been sent to the printer (118), processing advances to a softwareblock 811 where processing stops. If no reports were designated forprinting then processing advances directly from block 809 to 811 whereprocessing stops.

Thus, the reader will see that the system and method described abovetransforms extracted transaction data and information into detailedvaluations for specific elements of a business enterprise. The level ofdetail contained in the business valuations allows users of the systemto monitor and manage efforts to improve the value of the business in amanner that is superior to that available to users of traditionalaccounting systems and business valuation reports. The user also has theoption of examining the relationship between the calculated businessvalue and the market price of equity for the business.

While the above description contains many specificity's, these shouldnot be construed as limitations on the scope of the invention, butrather as an exemplification of one preferred embodiment thereof.Accordingly, the scope of the invention should be determined not by theembodiment illustrated, but by the appended claims and their legalequivalents.

1. A computer-implemented network model method, comprising: receivingfirst input data into an initial network model to develop an initialmodel configuration: receiving an input data set from said initial modelconfiguration and a second input data as inputs into a second modelstage to develop an improvement to said initial model configuration asan output, said second input data comprising one of said first inputdata, data not included in said first input data, and a combinationthereof: and receiving said second model stage output as an input into athird model stage to develop a final network model by evolving a networkmodel using a plurality of genetic algorithms where said final networkmodel supports a regression analysis.
 2. The method of claim 1, whereinsaid second model stage comprises an input/transform stage model thatcomprises: an input stage receiving said second input data and saidinput data set from the initial model configuration and a transformstage receiving an output of said input stage and transforming saidinput stage inputs into a summary comprising a second stage modeloutput.
 3. The method of claim 1, further comprising: avoiding an overlylong development time in a final network model development bydetermining when a stage of evolution does not add to an accuracy ofsaid final network model.
 4. The method of claim 1, wherein an inputdata set from said initial model configuration comprises the input datato said initial model configuration after training is complete.
 5. Themethod of claim 1, further comprising: using a plurality of independentsubpopulations to evolve a plurality of candidate network models with aplurality of genetic algorithms before using a crossover betweensubpopulations to identify a best fit model that combines the aspects ofeach candidate network model.
 6. The method of claim 1, furthercomprising: using a plurality of independent subpopulations to evolve aplurality of candidate network models with a plurality of geneticalgorithms to identify a set of one or more changes that will optimize anetwork model output value for a single criteria or multiple criteria.7. An apparatus to perform a network model method, said apparatuscomprising: means for receiving, processing and storing data; means forcompleting the three stage network model method of claim 1, and agraphic user interface to allow a user to identify one or more datasources for said network modeling method, and to at least one ofdisplay, print, and save to one of a printer, a data file, and anapplication program the output resulting from the final, third stagemodel where said final network model supports a regression analysis. 8.The apparatus of claim 7, wherein a second model stage comprises aninput/transform stage model that comprises: an input stage receivingsaid second input data and said input data set from the initial modelconfiguration and a transform stage receiving an output of said inputstage and transforming said input stage inputs into a summary comprisinga second stage model output.
 9. The apparatus of claim 7, furthercomprising: avoiding an overly long development time in a final networkmodel development by utilizing a means for determining when a stage ofevolution does not add to an accuracy of said final network model. 10.The apparatus of claim 7, wherein an input data set from said initialmodel configuration comprises the input data to said initial modelconfiguration after training is complete.
 11. The apparatus of claim 7,further comprising: means for using a plurality of independentsubpopulations to evolve a plurality of candidate network models with aplurality of genetic algorithms before using a crossover betweensubpopulations to identify a best fit final model that combines theaspects of each candidate network model.
 12. The apparatus of claim 7,further comprising: means for using a plurality of independentsubpopulations to evolve a plurality of candidate network models with aplurality of genetic algorithms to identify a set of one or more changesthat will optimize a network model output value for a single criteria ormultiple criteria.
 13. A machine-readable medium tangibly embodying aprogram of machine-readable instructions executable by a digitalprocessing apparatus to perform a network model method, comprising:receiving first input data into an initial network model to develop aninitial model configuration; receiving an input data set from saidinitial model configuration and a second input data as inputs into asecond model stage to develop an improvement to said initial modelconfiguration as an output, said second input data comprising one ofsaid first input data, data not included in said first input data, and acombination thereof; and receiving said second model stage output as aninput into a third model stage to develop a final network model byevolving a network model using a plurality of genetic algorithms wheresaid final network model supports a regression analysis.
 14. Themachine-readable medium of claim 13, wherein said second model stagecomprises an input/transform stage model that comprises: an input stagereceiving said second input data and said input data set from theinitial model configuration and a transform stage receiving an output ofsaid input stage and transforming said input stage inputs into a summarycomprising a second stage model output.
 15. The machine-readable mediumof claim 13, wherein the method further comprises: avoiding an overlylong development time in a final network model development bydetermining when a stage of evolution does not add to an accuracy ofsaid final network model.
 16. The machine-readable medium of claim 13,wherein an input data set from said initial model configurationcomprises the input data to said initial model configuration aftertraining is complete.
 17. The machine-readable medium of claim 13,wherein the method further comprises using a plurality of independentsubpopulations to evolve a plurality of candidate network models with aplurality of genetic algorithms before using a crossover betweensubpopulations to identify a best fit model that combines the aspects ofeach candidate network model.
 18. The machine-readable medium of claim13, wherein the method further comprises: using a plurality ofindependent subpopulations to evolve a plurality of candidate networkmodels with a plurality of genetic algorithms to identify a set of oneor more changes that will optimize a network model output value for asingle criteria or multiple criteria.
 19. A computing infrastructure,comprising the computer-readable code of claim 13 integrated into thecomputing apparatus of claim 7, wherein the code in combination with theapparatus is capable of performing the method of claim
 1. 20. Thecomputing infrastructure of claim 19, wherein a second model transformsone or more data inputs into a summary for use in the final model. 21.The computing infrastructure of claim 19 that is capable of performingthe method of claim
 5. 22. The computing infrastructure of claim 19 thatis capable of performing the method of claim
 6. 23. A computerimplemented neural network modeling method, comprising the network modelmethod of claim 1 wherein the initial model stage and the third modelstage are implemented using one or more neural network models.
 24. Themethod of claim 23, wherein said second model stage comprises aninput/transform stage that comprises: an input stage receiving saidsecond input data and said input data set from the initial modelconfiguration and a transform stage receiving an output of said inputstage and transforming said input stage inputs into a summary comprisinga second stale model output.
 25. The method of claim 23, furthercomprising: avoiding an overly long development time in a final networkmodel development by determining when a stage of evolution does not addto an accuracy of said final network model.
 26. The method of claim 23,further comprising: using a plurality of independent subpopulations toevolve a plurality of candidate neural network models with a pluralityof genetic algorithms before using a crossover between subpopulations toidentify a best fit neural network model that combines one or moreaspects of each candidate network model.
 27. The method of claim 26, themethod further comprising: using a fitness measure re-scaling toaccelerate the development of a best fit model.
 28. The method of claim23, further comprising: using a plurality of independent subpopulationsto evolve a plurality of candidate neural network models with aplurality of genetic algorithms to identify a set of one or more changesthat will optimize a neural network output value for a single criteriaor multiple criteria.
 29. An apparatus to perform a neural network modelmethods said apparatus comprising: the apparatus of claim 7 wherein theinitial model stage and the third model stage are implemented using oneor more neural network models.
 30. The apparatus of claim 29, whereinsaid second model stage comprises an input/transform stage thatcomprises: an input stage receiving said second input data and saidinput data set from the initial model configuration and a transformstage receiving an output of said input stage and transforming saidinput stage inputs into a summary comprising a second stage modeloutput.
 31. The apparatus of claim 29, further comprising: means foravoiding an overly long development time in a final network modeldevelopment by determining when a stage of evolution does not add to anaccuracy of said final neural network model.
 32. The apparatus of claim29, wherein an input data set from said initial neural network modelconfiguration comprises the input data to said initial neural networkmodel configuration after training is complete.
 33. The apparatus ofclaim 29, further comprising: means for using a plurality of independentsubpopulations to evolve a plurality of candidate neural network modelswith a plurality of genetic algorithms before using a crossover betweensubpopulations to identify a best fit neural network model that combinesone or more aspects of each candidate neural network model.
 34. Theapparatus of claim 29, further comprising: means for using a pluralityof independent subpopulations to evolve a plurality of candidate neuralnetwork models with a plurality of genetic algorithms to identify a setof one or more changes that will optimize a neural network model outputvalue for a single criteria or multiple criteria.
 35. A machine-readablemedium tangibly embodying a program of machine-readable instructionsexecutable by a digital processing apparatus to perform a neural networkmodel method, comprising: receiving first input data into an initialneural network model to develop an initial model configuration;receiving an input data set from said initial model configuration and asecond input data as inputs into a second model stage to develop animprovement to said initial model configuration as an output, saidsecond input data comprising one of said first input data, data notincluded in said first input data, and a combination thereof; andreceiving said second model stage output as an input into a third modelstage to develop a final neural network model by evolving a networkmodel using a plurality of genetic algorithms where said final neuralnetwork model supports a regression analysis.
 36. The machine-readablemedium of claim 35, wherein said second model stage comprises aninput/transform stage model that comprises: an input stage receivingsaid second input data and said input data set from the initial modelconfiguration and a transform stage receiving an output of said inputstage and transforming said input stage inputs into a summary comprisinga second stage model output.
 37. The machine-readable medium of claim35, wherein the method further comprises: avoiding an overly longdevelopment time in a final network model development by determiningwhen a stage of evolution does not add to an accuracy of said finalneural network model.
 38. The machine-readable medium of claim 35,wherein an input data set from said initial neural network modelconfiguration comprises the input data to said initial neural networkmodel configuration after training is complete.
 39. The machine-readablemedium of claim 35, wherein the method further comprises using aplurality of independent subpopulations to evolve a plurality ofcandidate neural network models with a plurality of genetic algorithmsbefore using a crossover between subpopulations to identify a best fitneural network model that combines one or more aspects of each candidateneural network model.
 40. The machine-readable medium of claim 35,wherein the method further comprises: using a plurality of independentsubpopulations to evolve a plurality of candidate neural network modelswith a plurality of genetic algorithms to identify a set of one or morechanges that will optimize a neural network model output value for asingle criteria or multiple criteria.
 41. A computing infrastructure,comprising computer-readable code integrated into a computing system,wherein the code in combination with the computing system is capable ofperforming the method of claim
 23. 42. The computing infrastructure ofclaim 41, wherein a second model transforms one or more data inputs intoa summary for use in the final model.
 43. The computing infrastructureof claim 41 that is capable of performing the method of claim
 26. 44.The computing infrastructure of claim 41 that is capable of performingthe method of claim
 28. 45. A computer implemented advanced networkmodeling method, comprising the network model method of claim 23 whereinthe second model stage is implemented using an induction algorithm. 46.The method of claim 45, wherein a second model stage comprises aninduction algorithm that receives a second input data and an input dataset from the initial model configuration and transforms said inputs intoa summary comprising a second stage model output.
 47. The method ofclaim 45, further comprising: avoiding an overly long development timein a final neural network model development by determining when a stageof evolution does not add to an accuracy of said final neural networkmodel.
 48. The method of claim 45, further comprising: using a pluralityof independent subpopulations to evolve a plurality of candidate neuralnetwork models with a plurality of genetic algorithms before using acrossover between subpopulations to identify a best fit neural networkmodel that combines one or more aspects of each candidate neural networkmodel.
 49. The method of claim 48, further comprising: using a fitnessmeasure re-scaling to accelerate the development of a best fit model.50. The method of claim 45, further comprising: using a plurality ofindependent subpopulations to evolve a plurality of candidate neuralnetwork models with a plurality of genetic algorithms to identify a setof one or more changes that will optimize a neural network output valuefor a single criteria or multiple criteria.
 51. An apparatus to performan advanced network model method, said apparatus comprising: theapparatus of claim 29 wherein the second model stage is implementedusing an induction algorithm.
 52. The apparatus of claim 51, wherein asecond model stage comprises an induction algorithm that receives asecond input data and an input data set from the initial modelconfiguration and transforms said inputs into a summary comprising asecond stage model output.
 53. The apparatus of claim 51, furthercomprising: means for avoiding an overly long development time in afinal network model development by determining when a stage of evolutiondoes not add to an accuracy of said final neural network model.
 54. Theapparatus of claim 51, wherein an input data set from said initialneural network model configuration comprises the input data to saidinitial neural network model configuration after training is complete.55. The apparatus of claim 51, further comprising: means for using aplurality of independent subpopulations to evolve a plurality ofcandidate neural network models with a plurality of genetic algorithmsbefore using a crossover between subpopulations to identify a best fitneural network model that combines one or more aspects of each candidateneural network model.
 56. The apparatus of claim 51, further comprising:means for using a plurality of independent subpopulations to evolve aplurality of candidate neural network models with a plurality of geneticalgorithms to identify a set of one or more changes that will optimize aneural network model output value for a single criteria or multiplecriteria.
 57. A machine-readable medium tangibly embodying a program ofmachine-readable instructions executable by a digital processingapparatus to perform a neural network model method, comprising:receiving first input data into an initial neural network model todevelop an initial model configuration; receiving an input data set fromsaid initial model configuration and a second input data as inputs intoa second, induction model stage to develop an improvement to saidinitial model configuration as an output, said second input datacomprising one of said first input data, data not included in said firstinput data and a combination thereof; and receiving said second modelstage output as an input into a third model stage to develop a finalneural network model by evolving a network model using a plurality ofgenetic algorithms where said final neural network model supports aregression analysis.
 58. The machine-readable medium of claim 57,wherein a second model stage comprises an induction algorithm thatreceives a second input data and an input data set from the initialmodel configuration and transforms said inputs into a summary comprisinga second stage model out ut.
 59. The machine-readable medium of claim57, the method further comprising: avoiding an overly long developmenttime in a final neural network model development by determining when astage of evolution does not add to an accuracy of said final neuralnetwork model.
 60. The machine-readable medium of claim 57, wherein aninput data set from said initial neural network model configurationcomprises the input data to said initial neural network modelconfiguration after training is complete.
 61. The machine-readablemedium of claim 57, further comprising: means for using a plurality ofindependent subpopulations to evolve a plurality of candidate neuralnetwork models with a plurality of genetic algorithms before using acrossover between subpopulations to identify a best fit neural networkmodel that combines one or more aspects of each candidate neural networkmodel.
 62. The machine-readable medium of claim 57, further comprising:means for using a plurality of independent subpopulations to evolve aplurality of candidate neural network models with a plurality of geneticalgorithms to identify a set of one or more changes that will optimize aneural network model output value for a single criteria or multiplecriteria.
 63. A computing infrastructure, comprising computer-readablecode integrated into a computing system, wherein the code in combinationwith the computing system is capable of performing the method of claim45.
 64. The computing infrastructure of claim 63, wherein an inductionmodel transforms one or more data inputs into a summary for use in thefinal model.
 65. The computing infrastructure of claim 63 that iscapable of performing the method of claim
 48. 66. The computinginfrastructure of claim 63 that is capable of performing the method ofclaim
 50. 67. A network model system, comprising: networked computerseach with a processor having circuitry to execute instructions; astorage device available to each processor with sequences ofinstructions stored therein, which when executed cause the processorsto: receive first input data into an initial network model to develop aninitial model configuration; receive an input data set from said initialmodel configuration and a second input data as inputs into a secondmodel stage to develop an improvement to said initial modelconfiguration as an output, said second input data comprising one ofsaid first input data, data not included in said first input data, and acombination thereof; and receive said second model stage output as aninput into a third model stage to develop a final network model byevolving a network model using a plurality of genetic algorithms wheresaid final network model supports a regression analysis.
 68. The systemof claim 67, wherein said second model stage comprises aninput/transform stage model that comprises: an input stage receivingsaid second input data and said input data set from the initial modelconfiguration and a transform stage receiving an output of said inputstage and transforming said input stage inputs into a summary comprisinga second stage model output.
 69. The system of claim 67, furthercomprising: the step of: avoiding an overly long development time in afinal network model development by determining when a stage of evolutiondoes not add to an accuracy of said final network model.
 70. The systemof claim 67, wherein an input data set from said initial modelconfiguration comprises the input data to said initial modelconfiguration after training is complete.
 71. The system of claim 67,the method further comprising: using a plurality of independentsubpopulations to evolve a plurality of candidate network models with aplurality of genetic algorithms before using a crossover betweensubpopulations to identify a best fit model that combines the aspects ofeach candidate network model.
 72. The system of claim 67, the methodfurther comprising: using a plurality of independent subpopulations toevolve a plurality of candidate network models with a plurality ofgenetic algorithms to identify a set of one or more changes that willoptimize a network model output value for a single criteria or multiplecriteria.
 73. A neural network model system, comprising: a computer witha processor having circuitry to execute instructions; a storage deviceavailable to said processor with sequences of instructions storedtherein, which when executed cause the processor to: receive first inputdata into an initial neural network model to develop an initial modelconfiguration; receive an input data set from said initial modelconfiguration and a second input data as inputs into a second modelstage to develop an improvement to said initial model configuration asan output, said second input data comprising one of said first inputdata, data not included in said first input data, and a combinationthereof; and receive said second model stage output as an input into athird model stage to develop a final neural network model by evolving aneural network model using a plurality of genetic algorithms where saidfinal neural network model supports a regression analysis.
 74. Thesystem of claim 73, wherein said second model stage comprises aninput/transform stage model that comprises: an input stage receivingsaid second input data and said input data set from the initial modelconfiguration and a transform stage receiving an output of said inputstage and transforming said input stage inputs into a summary comprisinga second stage model output.
 75. The system of claim 73, the methodfurther comprising: avoiding an overly long development time in a finalnetwork model development by determining when a stage of evolution doesnot add to an accuracy of said final network model.
 76. The system ofclaim 73, the method further comprising: using a plurality ofindependent subpopulations to evolve a plurality of candidate neuralnetwork models with a plurality of genetic algorithms before using acrossover between subpopulations to identify a best fit neural networkmodel that combines one or more aspects of each candidate network model.77. The system of claim 76, the method further comprising: using afitness measure re-scaling to accelerate the development of a best fitmodel.
 78. The system of claim 73, the method further comprising: usinga plurality of independent subpopulations to evolve a plurality ofcandidate neural network models with a plurality of genetic algorithmsto identify a set of one or more changes that will optimize a neuralnetwork output value for a single criteria or multiple criteria.
 79. Anadvanced network model system, comprising: networked computers each witha processor having circuitry to execute instructions; a storage deviceavailable to each processor with sequences of instructions storedtherein, which when executed cause the processors to: receive firstinput data into an initial neural network model to develop an initialmodel configuration; receive an input data set from said initial modelconfiguration and a second input data as inputs into a second, inductionmodel stage to develop an improvement to said initial modelconfiguration as an output, said second input data comprising one ofsaid first input data, data not included in said first input data, and acombination thereof; and receive said second model stage output as aninput into a third model stage to develop a final neural network modelby evolving a neural network model using a plurality of geneticalgorithms where said final neural network model supports a regressionanalysis.
 80. The system of claim 79, wherein a second model stagecomprises an induction algorithm that receives a second input data andan input data set from the initial model configuration and transformssaid inputs into an impact summary comprising a second stage modeloutput.
 81. The system of claim 79, the method further comprising:avoiding an overly long development time in a final network modeldevelopment by determining when a stage of evolution does not add to anaccuracy of said final network model.
 82. The system of claim 79, themethod further comprising: using a plurality of independentsubpopulations to evolve a plurality of candidate neural network modelswith a plurality of genetic algorithms before using a crossover betweensubpopulations to identify a best fit neural network model that combinesone or more aspects of each candidate network model.
 83. The system ofclaim 82, the method further comprising: using a fitness measurere-scaling to accelerate the development of a best fit model.
 84. Thesystem of claim 79, the method further comprising: using a plurality ofindependent subpopulations to evolve a plurality of candidate neuralnetwork models with a plurality of genetic algorithms to identify a setof one or more changes that will optimize a neural network output valuefor a single criteria or multiple criteria. 85-197. (canceled)